From 08ee574bde95a354e9299e9247ec713dd66c8de9 Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 12 Apr 2024 22:00:07 +0200 Subject: [PATCH 01/25] WIP: it runs through now with the newly defined ExternalInput block but the results are wrong. --- src/Neuroblox.jl | 2 +- src/blox/connections.jl | 24 ++++++++++++++++++++++++ src/blox/neural_mass.jl | 12 ++++-------- src/blox/sources.jl | 13 +++++++++++++ test/datafitting.jl | 13 ++++++++----- 5 files changed, 50 insertions(+), 14 deletions(-) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 1198d7e4..0ff71f6e 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -216,7 +216,7 @@ export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc export HebbianPlasticity, HebbianModulationPlasticity export Agent, ClassificationEnvironment, GreedyPolicy, reset! export LearningBlox -export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus +export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus, ExternalInput export PowerSpectrumBlox, BandPassFilterBlox export OUBlox, OUCouplingBlox export phase_inter, phase_sin_blox, phase_cos_blox diff --git a/src/blox/connections.jl b/src/blox/connections.jl index e3cc91df..e514946f 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -293,6 +293,30 @@ function (bc::BloxConnector)( accumulate_equation!(bc, eq) end +# additional dispatch to connect to a stimulus blox, first crafted for ExternalInput +function (bc::BloxConnector)( + bloxout::StimulusBlox, + bloxin::NeuralMassBlox; + weight=1 +) + + sys_out = get_namespaced_sys(bloxout) + sys_in = get_namespaced_sys(bloxin) + + w_name = Symbol("w_$(nameof(sys_out))_$(nameof(sys_in))") + if typeof(weight) == Num # Symbol + w = weight + else + w = only(@parameters $(w_name)=weight) + end + push!(bc.weights, w) + Main.foo[] = sys_out, bloxout.output + x = namespace_expr(bloxout.output, sys_out, nameof(sys_out)) + eq = sys_in.jcn ~ x*w + + accumulate_equation!(bc, eq) +end + # # Ok yes this is a bad dispatch but the whole compound blocks implementation is hacky and needs fixing @@ # # Opening an issue to loop back to this during clean up week # function (bc::BloxConnector)( diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index 06570940..530269a2 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -102,19 +102,15 @@ Arguments: """ struct LinearNeuralMass <: NeuralMassBlox - params output jcn odesystem namespace - function LinearNeuralMass(;name, namespace=nothing, C=0.0625) - p = paramscoping(C=C) - C = setmetadata(p[1], VariableTunable, false) # TODO: change paramscoping to be able to pass tunable flag - sts = @variables x(t)=0.0 [output=true] jcn(t)=0.0 [input=true] u(t) [irreducible=true, description="ext_input"] - eqs = [D(x) ~ jcn + C*u, - u ~ 1.0] + function LinearNeuralMass(;name, namespace=nothing) + sts = @variables x(t)=0.0 [output=true] jcn(t)=0.0 [input=true] + eqs = [D(x) ~ jcn] sys = System(eqs, name=name) - new(p, sts[1], sts[2], sys, namespace) + new(sts[1], sts[2], sys, namespace) end end diff --git a/src/blox/sources.jl b/src/blox/sources.jl index 1e692388..fa0b74cd 100644 --- a/src/blox/sources.jl +++ b/src/blox/sources.jl @@ -1,5 +1,18 @@ @parameters t +# Simple input blox +mutable struct ExternalInput <: StimulusBlox + namespace + output::Num + odesystem::ODESystem + function ExternalInput(;name, I=1.0, namespace=nothing) + sts = @variables u(t) [irreducible=true, description="ext_input"] + eqs = [u ~ I] + odesys = System(eqs, t, sts, []; name=name) + new(namespace, sts[1], odesys) + end +end + #CosineSource mutable struct CosineSource f::Num diff --git a/test/datafitting.jl b/test/datafitting.jl index 6d1e53f4..bf7f7991 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -3,24 +3,27 @@ using MAT ### Load data ### vars = matread(joinpath(@__DIR__, "spectralDCM_toydata.mat")); -data = DataFrame(vars["data"], :auto) # turn data into DataFrame -x = vars["x"] # initial conditions +data = DataFrame(vars["data"], :auto) # turn data into DataFrame +x = vars["x"] # initial conditions nrr = ncol(data) # number of recorded regions max_iter = 126 ########## assemble the model ########## g = MetaDiGraph() regions = Dict() -@parameters κ=0.0 [tunable = true] ϵ=0.0 [tunable=true] # define brain-wide decay parameter for hemodynamics +@parameters κ=0.0 [tunable = true] ϵ=0.0 [tunable=true] C=0.0625 [tunable = false] for ii = 1:nrr region = LinearNeuralMass(;name=Symbol("r$(ii)₊lm")) add_blox!(g, region) - regions[ii] = 2ii - 1 # store index of neural mass model + regions[ii] = nv(g) # store index of neural mass model + taskinput = ExternalInput(;name=Symbol("r$(ii)₊ei"), I=1.0) + add_blox!(g, taskinput) + add_edge!(g, nv(g), nv(g) - 1, Dict(:weight => C)) # add hemodynamic observer observer = BalloonModel(;name=Symbol("r$(ii)₊bm"), lnκ=κ, lnϵ=ϵ) add_blox!(g, observer) # connect observer with neuronal signal - add_edge!(g, 2ii - 1, 2ii, Dict(:weight => 1.0)) + add_edge!(g, nv(g) - 2, nv(g), Dict(:weight => 1.0)) end # add symbolic weights From 5d116825e4d6a04d4faaa5646a1d10f3fe221abf Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 12 Apr 2024 22:04:16 +0200 Subject: [PATCH 02/25] minor fixes --- src/blox/connections.jl | 2 +- test/datafitting.jl | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blox/connections.jl b/src/blox/connections.jl index e514946f..085a22e8 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -310,7 +310,7 @@ function (bc::BloxConnector)( w = only(@parameters $(w_name)=weight) end push!(bc.weights, w) - Main.foo[] = sys_out, bloxout.output + x = namespace_expr(bloxout.output, sys_out, nameof(sys_out)) eq = sys_in.jcn ~ x*w diff --git a/test/datafitting.jl b/test/datafitting.jl index 0756653f..38fa6380 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -11,7 +11,7 @@ max_iter = 128 g = MetaDiGraph() regions = Dict() -@parameters κ=0.0 [tunable = true] ϵ=0.0 [tunable=true] C=0.0625 [tunable = false] +@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=0.0625 [tunable = false] for ii = 1:nrr region = LinearNeuralMass(;name=Symbol("r$(ii)₊lm")) add_blox!(g, region) From 68634b499e3a94c397b40702dbd561d77061edd5 Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 12 Apr 2024 22:53:10 +0200 Subject: [PATCH 03/25] WIP: fix misalignment between states and jacobian (i.e. equations) of model --- test/datafitting.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/datafitting.jl b/test/datafitting.jl index 38fa6380..9bead5f8 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -11,7 +11,7 @@ max_iter = 128 g = MetaDiGraph() regions = Dict() -@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=0.0625 [tunable = false] +@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=1/16 [tunable = false] for ii = 1:nrr region = LinearNeuralMass(;name=Symbol("r$(ii)₊lm")) add_blox!(g, region) @@ -76,7 +76,7 @@ params_idx[:bold] = idx_bold # define prior variances paramvariance = copy(modelparam) paramvariance[:lnγ] = ones(Float64, nrr)./64.0; -paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0; +paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0; paramvariance[:lnβ] = ones(Float64, length(modelparam[:lnβ]))./64.0; for (k, v) in paramvariance if occursin("A[", string(k)) @@ -98,7 +98,7 @@ hyperpriors = Dict(:Πλ_pr => vars["ihC"]*ones(1, 1), # prior metaparameter p csdsetup = Dict(:p => 8, :freq => vec(vars["Hz"]), :dt => vars["dt"]); (state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, params_idx); -for iter in 1:128 +for iter in 1:max_iter state.iter = iter run_sDCM_iteration!(state, setup) print("iteration: ", iter, " - F:", state.F[end] - state.F[2], " - dF predicted:", state.dF[end], "\n") From 488a7bf6cc24526dde40f9d8a93190f7750613c0 Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Thu, 18 Apr 2024 00:25:22 +0200 Subject: [PATCH 04/25] problem fixed in a somewhat hacky way. Maybe revisit with MTK9 in place --- src/Neuroblox.jl | 2 +- src/blox/blox_utilities.jl | 44 +++++++++++++++++++++++++++------- src/datafitting/spectralDCM.jl | 35 +++++++++++++-------------- test/datafitting.jl | 26 ++++++++++---------- 4 files changed, 68 insertions(+), 39 deletions(-) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index eb47345e..29aeeeb6 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -206,7 +206,7 @@ export create_adjacency_edges!, adjmatrixfromdigraph export get_namespaced_sys, nameof export run_experiment!, run_trial! export addnontunableparams -export get_weights, get_dynamic_states +export get_weights, get_dynamic_states, get_idx_tagged_vars, get_eqidx_tagged_vars export BalloonModel, boldsignal_endo_balloon end diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl index 4c35ebdb..adca893b 100644 --- a/src/blox/blox_utilities.jl +++ b/src/blox/blox_utilities.jl @@ -252,18 +252,46 @@ end """ function get_dynamic_states(sys) sts = [] - idx_u = Int[] - idx_m = Int[] + idx = [] for (i, s) in enumerate(states(sys)) - if getdescription(s) == "ext_input" - push!(idx_u, i) - elseif getdescription(s) == "measurement" - push!(idx_m, i) - else + if !((getdescription(s) == "ext_input") || (getdescription(s) == "measurement")) push!(sts, s) + push!(idx, i) + end + end + return sts, idx +end + +function get_eqidx_tagged_vars(sys, tag) + idx = Int[] + vars = [] + eqs = equations(sys) + for s in states(sys) + if getdescription(s) == tag + push!(vars, s) + end + end + + for v in vars + for (i, e) in enumerate(eqs) + for s in Symbolics.get_variables(e) + if string(s) == string(v) + push!(idx, i) + end + end + end + end + return idx +end + +function get_idx_tagged_vars(sys, tag) + idx = Int[] + for (i, s) in enumerate(states(sys)) + if (getdescription(s) == tag) + push!(idx, i) end end - sts, idx_u, idx_m + return idx end """ diff --git a/src/datafitting/spectralDCM.jl b/src/datafitting/spectralDCM.jl index 6655365c..8976b361 100644 --- a/src/datafitting/spectralDCM.jl +++ b/src/datafitting/spectralDCM.jl @@ -100,12 +100,11 @@ function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np} return Eigen(evals, evecs) end -function transferfunction_fmri(ω, derivatives, params, params_idx) - ∂f = derivatives(params[params_idx[:dspars]]) - idx_ds = deleteat!([1:size(∂f, 1);], sort(vcat(params_idx[:bold], params_idx[:u]))) - ∂f∂x = ∂f[idx_ds, idx_ds] - ∂f∂u = ∂f[idx_ds, params_idx[:u]] - ∂g∂x = ∂f[params_idx[:bold], idx_ds] +function transferfunction_fmri(ω, derivatives, params, indices) + ∂f = derivatives(params[indices[:dspars]]) + ∂f∂x = ∂f[indices[:sts], indices[:sts]] + ∂f∂u = ∂f[indices[:sts], indices[:u]] + ∂g∂x = ∂f[indices[:bold], indices[:sts]] F = eigen(∂f∂x) Λ = F.values @@ -141,14 +140,14 @@ end Gn in the code corresponds to Ge in the paper, i.e. the observation noise. In the code global and local components are defined, no such distinction is discussed in the paper. In fact the parameter γ, corresponding to local component is not present in the paper. """ -function csd_approx(ω, derivatives, params, params_idx) +function csd_approx(ω, derivatives, params, indices) # priors of spectral parameters # ln(α) and ln(β), region specific fluctuations: ln(γ) nω = length(ω) - nd = length(params_idx[:lnγ]) - α = params[params_idx[:lnα]] - β = params[params_idx[:lnβ]] - γ = params[params_idx[:lnγ]] + nd = length(indices[:lnγ]) + α = params[indices[:lnα]] + β = params[indices[:lnβ]] + γ = params[indices[:lnγ]] # define function that implements spectra given in equation (2) of the paper "A DCM for resting state fMRI". @@ -174,7 +173,7 @@ function csd_approx(ω, derivatives, params, params_idx) Gn[:,j,i] = Gn[:,i,j] end end - S = transferfunction_fmri(ω, derivatives, params, params_idx) # This is K(ω) in the equations of the spectral DCM paper. + S = transferfunction_fmri(ω, derivatives, params, indices) # This is K(ω) in the equations of the spectral DCM paper. # predicted cross-spectral density G = zeros(eltype(S), nω, nd, nd); @@ -185,8 +184,8 @@ function csd_approx(ω, derivatives, params, params_idx) return G + Gn end -@views function csd_fmri_mtf(freqs, p, derivatives, params, params_idx) # alongside the above realtes to spm_csd_fmri_mtf.m - G = csd_approx(freqs, derivatives, params, params_idx) +@views function csd_fmri_mtf(freqs, p, derivatives, params, indices) # alongside the above realtes to spm_csd_fmri_mtf.m + G = csd_approx(freqs, derivatives, params, indices) dt = 1/(2*freqs[end]) # the following two steps are very opaque. They are taken from the SPM code but it is unclear what the purpose of this transformation and back-transformation is # in particular it is also unclear why the order of the MAR is reduced by 1. My best guess is that this procedure smoothens the results. @@ -280,7 +279,7 @@ end """ - function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, params_idx) + function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, indices) Interface function to performs variational inference to fit model parameters to empirical cross spectral density. The current implementation provides a Variational Laplace fit (see function above `variationalbayes`). @@ -300,9 +299,9 @@ end - `hyperpriors` : dataframe of parameters with the following columns: -- `Πλ_pr` : prior precision matrix for λ hyperparameter(s) -- `μλ_pr` : prior mean(s) for λ hyperparameter(s) - - `params_idx` : indices to separate model parameters from other parameters. Needed for the computation of AD gradient. + - `indices` : indices to separate model parameters from other parameters. Needed for the computation of AD gradient. """ -function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params_idx) +function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, indices) # compute cross-spectral density dt = csdsetup[:dt]; # order of MAR. Hard-coded in SPM12 with this value. We will use the same for now. ω = csdsetup[:freq]; # frequencies at which the CSD is evaluated @@ -323,7 +322,7 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params nq = 1 # TODO: this is hard-coded, need to make this compliant with csd_Q nh = size(Q, 3) # number of precision components (this is the same as above, but may differ) - f = params -> csd_fmri_mtf(ω, p, derivatives, params, params_idx) + f = params -> csd_fmri_mtf(ω, p, derivatives, params, indices) np = length(μθ_pr) # number of parameters ny = length(y_csd) # total number of response variables diff --git a/test/datafitting.jl b/test/datafitting.jl index 9bead5f8..c052de63 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -41,13 +41,15 @@ end neuronmodel = structural_simplify(neuronmodel) # attribute initial conditions to states -ds_states, idx_u, idx_bold = get_dynamic_states(neuronmodel) -initcond = OrderedDict(ds_states .=> 0.0) +sts, idx_sts = get_dynamic_states(neuronmodel) +idx_u = get_idx_tagged_vars(neuronmodel, "ext_input") # get index of external input state +idx_bold = get_eqidx_tagged_vars(neuronmodel, "measurement") # get index of equation of bold state +initcond = OrderedDict(sts .=> 0.0) rnames = [] -map(x->push!(rnames, split(string(x), "₊")[1]), ds_states); +map(x->push!(rnames, split(string(x), "₊")[1]), sts); rnames = unique(rnames); for (i, r) in enumerate(rnames) - for (j, s) in enumerate(ds_states[r .== map(x -> x[1], split.(string.(ds_states), "₊"))]) + for (j, s) in enumerate(sts[r .== map(x -> x[1], split.(string.(sts), "₊"))]) initcond[s] = x[i, j] end end @@ -57,22 +59,22 @@ for par in tunable_parameters(neuronmodel) modelparam[par] = Symbolics.getdefaultval(par) end np = length(modelparam) -params_idx = Dict(:dspars => collect(1:np)) +indices = Dict(:dspars => collect(1:np)) # Noise parameter mean modelparam[:lnα] = [0.0, 0.0]; # intrinsic fluctuations, ln(α) as in equation 2 of Friston et al. 2014 n = length(modelparam[:lnα]); -params_idx[:lnα] = collect(np+1:np+n); +indices[:lnα] = collect(np+1:np+n); np += n; modelparam[:lnβ] = [0.0, 0.0]; # global observation noise, ln(β) as above n = length(modelparam[:lnβ]); -params_idx[:lnβ] = collect(np+1:np+n); +indices[:lnβ] = collect(np+1:np+n); np += n; modelparam[:lnγ] = zeros(Float64, nrr); # region specific observation noise -params_idx[:lnγ] = collect(np+1:np+nrr); +indices[:lnγ] = collect(np+1:np+nrr); np += nrr -params_idx[:u] = idx_u -params_idx[:bold] = idx_bold - +indices[:u] = idx_u +indices[:bold] = idx_bold +indices[:sts] = idx_sts # define prior variances paramvariance = copy(modelparam) paramvariance[:lnγ] = ones(Float64, nrr)./64.0; @@ -97,7 +99,7 @@ hyperpriors = Dict(:Πλ_pr => vars["ihC"]*ones(1, 1), # prior metaparameter p csdsetup = Dict(:p => 8, :freq => vec(vars["Hz"]), :dt => vars["dt"]); -(state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, params_idx); +(state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, indices); for iter in 1:max_iter state.iter = iter run_sDCM_iteration!(state, setup) From 28e55921954a9fe2fe28026370ca873d263e35e2 Mon Sep 17 00:00:00 2001 From: Mason Protter Date: Wed, 1 May 2024 17:05:09 +0200 Subject: [PATCH 05/25] Bump to MTK v9 (#342) * bump to MTK v9 * some renames * more fixes * fix * more fixes to reinforcement learning * deepcopy -> copy * fix plasticity tests * get datafitting tests working * fix update to datafitting * bump version * improve type stability of spectralDCM code * added an external input to LinearNeuralMass as well as the necessary machinery to deal with it in blox_utilities.jl: function get_states_without_drive * finished implementing new feature: sDCM can now potentially deal with external input. Implementation differs from SPM12 since C parameter is not being overwritten when transfer function is being computed but the user-set value is kept. * merged bold model into BalloonModel. This makes the code more robust w.r.t. potential changes in MTK and pecularities about model assembly. It also deprecates the get_hemodynamic_observers function. * implemented new approach to deal with measurements (such as bold signal) based on Haris's comment to use irreducible to maintain measurement equations which are algebraic equations of dynamic variables. This makes in particular transferfunction_fmri much more elegant and robust. * added back OU-JansenRit test * LIF Neuron implementation Basic LIF Neuron with tests using MTK * Added QIFNeuron support Also added TestSet for QIF neurons * Starter Izhikevich commit Still under construction * IF Neuron implementation Simple IF addition * Add helper function `get_connection_rule` added to blox_utilities to mirror other helper functions for BloxConnector * Izhikevich Neuron addition Basic Izhikevich neurons from Chen and Campbell added * Updates per comments *Fixed the `I_syn` redundancies *Moved the for `:connection_rule` to `get_connection_rule` *Added parameter ranges for the GUI in the comments * Fix get_connection_rule Only returns `rhs` now * added TAN to GUI.jl * added QIFNeuron to GUI * prefixed kappa and epsilon with ln * Add MKL to Neuroblox. Fix in test datafitting.jl to ensure consistent sorting of values of initial conditions. * moved VL structs from Neuroblox.jl to spectralDCM.jl * added descriptions of elements of VL structs * removing the deprecated codebase * Old neuron cleanup Delete old IF, LIF, QIF blox and associated helper functions and tests. Also remove `synaptic_network.jl` as it is fully replaced by `BloxConnector` * Update components.jl * bump to MTK v9 * some renames * more fixes * fix * more fixes to reinforcement learning * deepcopy -> copy * fix plasticity tests * get datafitting tests working * fix update to datafitting * bump version * improve type stability of spectralDCM code * oops * Update IF/LIF Include citations and docstring formatting. Still finishing up QIF and Izhikevich * Reminder for IF connections Synaptic conductances for these need to be scaled appropriately! * QIF updates Also cleanup * fixes for rebase * oops, mark broken test * remove the `.odesystem` test * more states -> unknowns whack-a-mole --------- Co-authored-by: david-hofmann Co-authored-by: David Hofmann <1681922+david-hofmann@users.noreply.github.com> Co-authored-by: Helmut Strey Co-authored-by: agchesebro <76024790+agchesebro@users.noreply.github.com> Co-authored-by: Helmut H. Strey --- Project.toml | 5 +- README.md | 1 + examples/RF_learning_using_BLOX.jl | 2 +- .../whole_brain_larter_breakspear.jl | 4 +- src/Neuroblox.jl | 15 +- src/Neurographs.jl | 14 +- src/blox/.DS_Store | Bin 6148 -> 0 bytes src/blox/blox_utilities.jl | 6 +- src/blox/canonicalmicrocircuit.jl | 2 +- src/blox/connections.jl | 14 +- src/blox/neural_mass.jl | 21 +- src/blox/neuron_models.jl | 263 ++++++------------ src/blox/reinforcement_learning.jl | 23 +- src/blox/rl_blox.jl | 2 - src/blox/sources.jl | 2 - src/blox/subcortical_blox.jl | 28 +- src/blox/synaptic_network.jl | 20 -- src/datafitting/spectralDCM.jl | 77 +++-- src/measurementmodels/fmri.jl | 55 +++- test/components.jl | 221 +++++++-------- test/datafitting.jl | 13 +- test/final_dde_before_merge.jl | 4 +- test/plasticity.jl | 12 +- test/reinforcement_learning.jl | 23 +- test/runtests.jl | 3 +- test/source_components.jl | 4 +- 26 files changed, 379 insertions(+), 455 deletions(-) delete mode 100644 src/blox/.DS_Store delete mode 100644 src/blox/synaptic_network.jl diff --git a/Project.toml b/Project.toml index 67aec84e..29cc312a 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "Neuroblox" uuid = "769b91e5-4c60-41ee-bfae-153c84203cb2" repo = "https://github.com/Neuroblox/Neuroblox.jl.git" -version = "0.3.1" +version = "0.4.0" [deps] AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c" @@ -21,7 +21,6 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" Glob = "c27321d9-0574-5035-807b-f59d2c89b15c" Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" -IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173" Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" @@ -79,7 +78,7 @@ JLD2 = "0.4" MAT = "0.10" MLJ = "0.20" MetaGraphs = "0.7" -ModelingToolkit = "8" +ModelingToolkit = "9.6" ModelingToolkitStandardLibrary = "2" MuladdMacro = "0.2" NaNStatistics = "0.6" diff --git a/README.md b/README.md index ced4e6cf..95482c1c 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ + # Neuroblox.jl [![CI](https://github.com/Neuroblox/Neuroblox.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/Neuroblox/Neuroblox.jl/actions/workflows/CI.yml) diff --git a/examples/RF_learning_using_BLOX.jl b/examples/RF_learning_using_BLOX.jl index d57ef9f7..1f197551 100644 --- a/examples/RF_learning_using_BLOX.jl +++ b/examples/RF_learning_using_BLOX.jl @@ -158,7 +158,7 @@ end begin #extract membrane voltages of every neuron getsys=agent.odesystem; - st=states(getsys) + st=unknowns(getsys) vlist=Int64[] for ii = 1:length(st) if contains(string(st[ii]), "V(t)") diff --git a/examples/larter_breakspear/whole_brain_larter_breakspear.jl b/examples/larter_breakspear/whole_brain_larter_breakspear.jl index b8e1754a..d5811808 100644 --- a/examples/larter_breakspear/whole_brain_larter_breakspear.jl +++ b/examples/larter_breakspear/whole_brain_larter_breakspear.jl @@ -98,7 +98,7 @@ prob = ODEProblem(sys, u₀, (0.0, sim_dur), []) # More interesting is to choose the plot from a specific region and see the results. Here, we'll plot a specific region's average voltage. # First, confirm the region (left orbitofrontal cortex) -states(sys)[64] # Should give L_OFC₊V(t) +unknowns(sys)[64] # Should give L_OFC₊V(t) # Next plot just this variable #plot(sol, idxs=(64)) @@ -141,4 +141,4 @@ omit_idx = Int(round(90/(TR/1000))) bold = bold[:, omit_idx:end] # Plot an example region to get a sense of what the BOLD signal looks like -# plot(bold[1, :]) \ No newline at end of file +# plot(bold[1, :]) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 51106ec7..98f1715a 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -2,6 +2,9 @@ module Neuroblox using Reexport @reexport using ModelingToolkit +const t = ModelingToolkit.t_nounits +const D = ModelingToolkit.D_nounits +export t, D @reexport using ModelingToolkitStandardLibrary.Blocks @reexport using Graphs: add_edge! @@ -37,7 +40,6 @@ using ModelingToolkit: get_namespace, get_systems, isparameter, import ModelingToolkit: inputs, nameof, outputs, getdescription using Symbolics: @register_symbolic, getdefaultval -using IfElse using DelimitedFiles: readdlm using CSV: read @@ -96,7 +98,6 @@ include("blox/neural_mass.jl") include("blox/cortical_blox.jl") include("blox/canonicalmicrocircuit.jl") include("blox/neuron_models.jl") -include("blox/synaptic_network.jl") include("blox/van_der_pol.jl") include("blox/ts_outputs.jl") include("blox/sources.jl") @@ -120,7 +121,7 @@ end function simulate(blox::CorticalBlox, u0, timespan, p, solver = AutoVern7(Rodas4()); kwargs...) prob = ODEProblem(blox.odesystem, u0, timespan, p) sol = solve(prob, solver; kwargs...) #pass keyword arguments to solver - statesV = [s for s in states(blox.odesystem) if contains(string(s),"V")] + statesV = [s for s in unknowns(blox.odesystem) if contains(string(s),"V")] vsol = sol[statesV] vmean = vec(mean(hcat(vsol...),dims=2)) df = DataFrame(sol) @@ -144,7 +145,7 @@ And outputs: u0 : Float64 vector of initial conditions """ function random_initials(odesys::ODESystem, blox) - odestates = states(odesys) + odestates = unknowns(odesys) u0 = Float64[] init_dict = Dict{Num,Tuple{Float64,Float64}}() @@ -183,9 +184,9 @@ function __init__() end export JansenRitSPM12, next_generation, qif_neuron, if_neuron, hh_neuron_excitatory, - hh_neuron_inhibitory, synaptic_network, van_der_pol -export IFNeuronBlox, LIFNeuronBlox, QIFNeuronBlox, HHNeuronExciBlox, HHNeuronInhibBlox, - CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical, IFNeuron, LIFNeuron, QIFNeuron, IzhikevichNeuron + hh_neuron_inhibitory, van_der_pol +export HHNeuronExciBlox, HHNeuronInhibBlox, IFNeuron, LIFNeuron, QIFNeuron, IzhikevichNeuron, + CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical export LinearNeuralMass, HarmonicOscillator, JansenRit, WilsonCowan, LarterBreakspear, NextGenerationBlox, NextGenerationResolvedBlox, NextGenerationEIBlox export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc export HebbianPlasticity, HebbianModulationPlasticity diff --git a/src/Neurographs.jl b/src/Neurographs.jl index 412b24d0..ae1633d4 100644 --- a/src/Neurographs.jl +++ b/src/Neurographs.jl @@ -29,6 +29,7 @@ get_sys(g::MetaDiGraph) = get_sys.(get_blox(g)) function connector_from_graph(g::MetaDiGraph) bloxs = get_blox(g) link = BloxConnector(bloxs) + for v in vertices(g) b = get_prop(g, v, :blox) for vn in inneighbors(g, v) @@ -37,7 +38,6 @@ function connector_from_graph(g::MetaDiGraph) link(bn, b; kwargs...) end end - return link end @@ -59,29 +59,23 @@ function system_from_graph(g::MetaDiGraph, p::Vector{Num}; name, t_block=missing end function system_from_graph(g::MetaDiGraph, bc::BloxConnector; name, t_block=missing) - @variables t blox_syss = get_sys(g) - connection_eqs = get_equations_with_state_lhs(bc) - - cbs = identity.(get_callbacks(g, bc; t_block)) + cbs = identity.(get_callbacks(g, bc; t_block)) return compose(ODESystem(connection_eqs, t, [], params(bc); name, discrete_events = cbs), blox_syss) end function system_from_graph(g::MetaDiGraph, bc::BloxConnector, p::Vector{Num}; name, t_block=missing) - @variables t + blox_syss = get_sys(g) connection_eqs = get_equations_with_state_lhs(bc) - cbs = identity.(get_callbacks(g, bc; t_block)) - return compose(ODESystem(connection_eqs, t, [], vcat(params(bc), p); name, discrete_events = cbs), blox_syss) end function system_from_parts(parts::AbstractVector; name) - @variables t return compose(ODESystem(Equation[], t, [], []; name), get_sys.(parts)) end @@ -181,4 +175,4 @@ function create_adjacency_edges!(g::MetaDiGraph, adj_matrix::Matrix{T}, delay_ma end end end -end \ No newline at end of file +end diff --git a/src/blox/.DS_Store b/src/blox/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 [sys_matr_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)] - cb_strios = [t_event] => [sys_strios_in.H ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)] + cb_matr = [t_event] => [sys_matr_in.H ~ ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)] + cb_strios = [t_event] => [sys_strios_in.H ~ ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, 0, 1)] # HACK: H should be reset to 1 at the beginning of each trial # Such callbacks should be moved to RL-specific functions like `run_experiment!` @@ -509,7 +509,7 @@ function (bc::BloxConnector)( sys_neuron = get_namespaced_sys(neuron) # Large negative current added to shut down the Striatum spiking neurons. # Value is hardcoded for now, as it's more of a hack, not user option. - cb_neuron = [t_event] => [sys_neuron.I_bg ~ IfElse.ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, -2, 0)] + cb_neuron = [t_event] => [sys_neuron.I_bg ~ ifelse(sys_matr_out.H*sys_matr_out.jcn > sys_matr_in.H*sys_matr_in.jcn, -2, 0)] # lateral inhibition current I_bg should be set to 0 at the beginning of each trial cb_neuron_init = [0.1] => [sys_neuron.I_bg ~ 0] push!(bc.events, cb_neuron) @@ -585,7 +585,7 @@ function (bc::BloxConnector)( end t_event = get_event_time(kwargs, nameof(discr_out), nameof(discr_in)) - cb = [t_event+sqrt(eps(t_event))] => (sample_affect!, [], [sys_out.κ, sys_out.jcn, sys_in.TAN_spikes], nothing) + cb = [t_event+sqrt(eps(t_event))] => (sample_affect!, [], [sys_out.κ, sys_out.jcn, sys_in.TAN_spikes], []) push!(bc.events, cb) eq = sys_in.jcn ~ w*sys_in.TAN_spikes @@ -602,7 +602,7 @@ function (bc::BloxConnector)( sys_in = get_namespaced_sys(discr_in) t_event = get_event_time(kwargs, nameof(discr_out), nameof(discr_in)) - cb = [t_event] => [sys_in.H ~ IfElse.ifelse(sys_out.H*sys_out.jcn > sys_in.H*sys_in.jcn, 0, 1)] + cb = [t_event] => [sys_in.H ~ ifelse(sys_out.H*sys_out.jcn > sys_in.H*sys_in.jcn, 0, 1)] push!(bc.events, cb) end @@ -701,4 +701,4 @@ function (bc::BloxConnector)( end accumulate_equation!(bc, eq) -end \ No newline at end of file +end diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index 06570940..d00294ab 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -1,6 +1,3 @@ -@parameters t -D = Differential(t) - mutable struct NextGenerationBlox <: NeuralMassBlox C::Num Δ::Num @@ -113,7 +110,7 @@ struct LinearNeuralMass <: NeuralMassBlox sts = @variables x(t)=0.0 [output=true] jcn(t)=0.0 [input=true] u(t) [irreducible=true, description="ext_input"] eqs = [D(x) ~ jcn + C*u, u ~ 1.0] - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) new(p, sts[1], sts[2], sys, namespace) end end @@ -152,7 +149,7 @@ struct HarmonicOscillator <: NeuralMassBlox ω, ζ, k, h = p eqs = [D(x) ~ y-(2*ω*ζ*x)+ k*(2/π)*(atan((jcn)/h)) D(y) ~ -(ω^2)*x] - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) new(p, sts[1], sts[3], sys, namespace) end end @@ -212,7 +209,7 @@ struct JansenRit <: NeuralMassBlox sts = @variables x(t)=1.0 [output=true] y(t)=1.0 jcn(t)=0.0 [input=true] eqs = [D(x) ~ y - ((2/τ)*x), D(y) ~ -x/(τ*τ) + (H/τ)*((2*λ)/(1 + exp(-r*(jcn))) - λ)] - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) #can't use outputs because x(t) is Num by then #wrote inputs similarly to keep consistent return new(p, sts[1], sts[3], sys, namespace) @@ -220,16 +217,20 @@ struct JansenRit <: NeuralMassBlox sts = @variables x(..)=1.0 [output=true] y(t)=1.0 jcn(t)=0.0 [input=true] eqs = [D(x(t)) ~ y - ((2/τ)*x(t)), D(y) ~ -x(t)/(τ*τ) + (H/τ)*((2*λ)/(1 + exp(-r*(jcn))) - λ)] - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) #can't use outputs because x(t) is Num by then #wrote inputs similarly to keep consistent return new(p, sts[1], sts[3], sys, namespace) end + sys = System(eqs, t, name=name) + #can't use outputs because x(t) is Num by then + #wrote inputs similarly to keep consistent + return new(p, sts[1], sts[3], sys, namespace) end end """ - WilsonCown(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η) + WilsonCowan(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η) Create a standard Wilson Cowan blox. The formal definition of this blox is: @@ -271,7 +272,7 @@ struct WilsonCowan <: NeuralMassBlox sts = @variables E(t)=1.0 [output=true] I(t)=1.0 jcn(t)=0.0 [input=true] #P(t)=0.0 eqs = [D(E) ~ -E/τ_E + 1/(1 + exp(-a_E*(c_EE*E - c_IE*I - θ_E + η*(jcn)))), #old form: D(E) ~ -E/τ_E + 1/(1 + exp(-a_E*(c_EE*E - c_IE*I - θ_E + P + η*(jcn)))), D(I) ~ -I/τ_I + 1/(1 + exp(-a_I*(c_EI*E - c_II*I - θ_I)))] - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) new(p, sts[1], sts[3], sys, namespace) end end @@ -350,7 +351,7 @@ struct LarterBreakspear <: NeuralMassBlox m_Ca ~ 0.5*(1 + tanh((V-T_Ca)/δ_Ca)), m_Na ~ 0.5*(1 + tanh((V-T_Na)/δ_Na)), m_K ~ 0.5*(1 + tanh((V-T_K)/δ_K))] - sys = System(eqs; name=name) + sys = System(eqs, t; name=name) new(p, sts[5], sts[4], sys, namespace) end end diff --git a/src/blox/neuron_models.jl b/src/blox/neuron_models.jl index c8167e72..0eba160b 100644 --- a/src/blox/neuron_models.jl +++ b/src/blox/neuron_models.jl @@ -1,128 +1,5 @@ abstract type AbstractInhNeuronBlox <: AbstractNeuronBlox end abstract type AbstractExciNeuronBlox <: AbstractNeuronBlox end -@parameters t -D = Differential(t) - -#Quadratic Integrate and Fire neurons -mutable struct QIFNeuronBlox <: AbstractNeuronBlox - # all parameters are Num as to allow symbolic expressions - C::Num - E_syn::Num - G_syn::Num - ω::Num - τ::Num - connector::Num - noDetail::Vector{Num} - detail::Vector{Num} - initial::Dict{Num, Tuple{Float64, Float64}} - odesystem::ODESystem - function QIFNeuronBlox(;name,C=1.0,E_syn=0, G_syn=1,ω=0,τ=10) - - sts = @variables V(t) = -70.0 G(t)=0.0 z(t)=0.0 I_syn(t)=0.0 jcn(t)=0.0 - ps = @parameters C=C ω=ω I_in=(ω*C/2)^2 Eₘ=0.0 Vᵣₑₛ=-70.0 θ=25 τ₁=τ τ₂=τ E_syn=E_syn G_syn=G_syn - - eqs = [ - D(V) ~ ((V-Eₘ)^2+I_in+I_syn)/C, - D(G)~(-1/τ₂)*G + z, - D(z)~(-1/τ₁)*z - ] - ev = [V~θ] => [V~Vᵣₑₛ,z~G_syn] - odesys = ODESystem(eqs,t,sts,ps,continuous_events=[ev];name=name) - new(C, E_syn, G_syn, ω, τ, odesys.G, - [odesys.V],[odesys.V, odesys.G],Dict{Num, Tuple{Float64, Float64}}(),odesys) - end -end - -# Leaky Integrate and Fire neuron with synaptic dynamics -# Deprecated in favor of LIFNeuron below -mutable struct IFNeuronBlox <: AbstractNeuronBlox - # all parameters are Num as to allow symbolic expressions - C::Num - E_syn::Num - G_syn::Num - I_in::Num - freq::Num - phase::Num - τ::Num - connector::Num - noDetail::Vector{Num} - detail::Vector{Num} - initial::Dict{Num, Tuple{Float64, Float64}} - odesystem::ODESystem - function IFNeuronBlox(;name,C=1.0,E_syn=0,G_syn=0.2,I_in=0,freq=0,phase=0,τ=10) - - sts = @variables V(t) = -70.00 G(t)=0.0 z(t)=0.0 spt(t)=0.0 Cₜ(t) = 0.0 I_syn(t)=0 jcn(t)=0.0 - ps = @parameters C=C I_in = I_in Eₘ = -70.0 Rₘ = 100.0 θ = -50.0 τ₁=0.1 τ₂=τ E_syn=E_syn G_syn=G_syn phase=phase τᵣ=3 - - eqs = [ - D(V) ~ (-(V-Eₘ)/Rₘ + I_in*(sin((t*freq*2*pi/1000)+phase)+1) + I_syn)/(C+Cₜ), - D(G)~(-1/τ₂)*G + z, - D(z)~(-1/τ₁)*z, - D(spt)~0, - D(Cₜ)~(-1/τᵣ)*Cₜ - ] - ev = [V~θ] => [V~Eₘ,z~G_syn,Cₜ~10] - odesys = ODESystem(eqs,t,sts,ps,continuous_events=[ev];name=name) - new(C, E_syn, G_syn, I_in, freq, phase, τ, odesys.G, - [odesys.V],[odesys.V, odesys.G],Dict{Num, Tuple{Float64, Float64}}(),odesys) - end -end - -""" -Standard Leaky Integrate and Fire neuron model. - -variables: - V(t): Membrane voltage - jcn: Input from other neurons -parameters: - I_in: Input current - V_L: Resting state potential - τ: Membrane timescale - R: Membrane resistance - θ: Spike threshold - st: Last spike time - strain: Spike train -returns: - an ODE System -""" -mutable struct LIFNeuronBlox <: AbstractComponent - I_in::Num - V_L::Num - τ::Num - R::Num - θ::Num - connector::Num - noDetail::Vector{Num} - detail::Vector{Num} - initial::Dict{Num, Tuple{Float64, Float64}} - odesystem::ODESystem - function LIFNeuronBlox(;name, I_in=0, V_L=-70.0, τ=10.0, R=100.0, θ = -10.0) - sts = @variables V(t) = -70.0 jcn(t) = 0.0 - par = @parameters I_in=I_in V_L=V_L R=R τ=τ st=-Inf strain=[] - eqs = [ - D(V) ~ (-V + V_L + R*(I_in + jcn))/τ - ] - - function lif_affect!(integ, u, p, ctx) - integ.u[u.V] = integ.p[p.V_L] - integ.p[p.st] = integ.t - push!(integ.p[p.strain], integ.t) - end - - spike = [V ~ θ] => (lif_affect!, [V], [V_L, st, strain], nothing) - - odesys = ODESystem(eqs, t, sts, par; continuous_events=spike, name=name) - new(I_in, V_L, τ, R, θ, odesys.V, [odesys.V],[odesys.V],Dict{Num, Tuple{Float64, Float64}}(),odesys) - end -end - -function spike_affect!(integ, u, p, ctx) - du = SciMLBase.get_du(integ) - if du[u.V] > 0 - integ.u[u.spikes_cumulative] += 1 - integ.u[u.spikes_window] += 1 - end -end struct HHNeuronExciBlox <: AbstractExciNeuronBlox odesystem @@ -288,11 +165,35 @@ struct HHNeuronInhibBlox <: AbstractInhNeuronBlox end end +""" + IFNeuron(name, namespace, C, θ, Eₘ, I_in) + + Create a basic integrate-and-fire neuron. + This follows Lapicque's equation (see Abbott [1], with parameters chosen to match the LIF/QIF neurons implemented as well): + +```math +\\frac{dV}{dt} = \\frac{I_{in} + jcn}{C} +``` +where ``jcn`` is any input to the blox. + +Arguments: +- name: Name given to ODESystem object within the blox. +- namespace: Additional namespace above name if needed for inheritance. +- C: Membrane capicitance (μF). +- θ: Threshold voltage (mV). +- Eₘ: Resting membrane potential (mV). +- I_in: External current input (μA). + +References: +1. Abbott, L. Lapicque's introduction of the integrate-and-fire model neuron (1907). Brain Res Bull 50, 303-304 (1999). +""" + # Paramater bounds for GUI -# C = [0.1, 100] -# θ = [-65, -45] -# Eₘ = [-100, -55] - If Eₘ >= θ obvious instability -# I_in = [-25, 25] +# C = [0.1, 100] μF +# θ = [-65, -45] mV +# Eₘ = [-100, -55] mV - If Eₘ >= θ obvious instability +# I_in = [-2.5, 2.5] μA +# Remember: synaptic weights need to be in μA/mV, so they're very small! struct IFNeuron <: AbstractNeuronBlox params output @@ -316,19 +217,42 @@ struct IFNeuron <: AbstractNeuronBlox end end -# Paramater bounds for GUI -# C = [0.1, 100] -# Eₘ = [-100, -55] -# Rₘ = [50, 200] -# τ₁ = [0.01, 1] -# τ₂ = [1.0, 10.0] -# τᵣ = [1, 10] -# θ = [-65, -45] -# E_syn = [-10, 10] -# G_syn = [0.1, 1] -# I_in = [-25, 25] -# freq = [0, 100] -# phase = [0, 2π] +""" + LIFNeuron(name, namespace, C, θ, Eₘ, I_in) + + Create a leaky integrate-and-fire neuron. + This largely follows the formalism and parameters given in Chapter 8 of Sterratt et al. [1], with the following equations: + +```math +\\frac{dV}{dt} = \\frac{\\frac{-(V-E_m)}{R_m} + I_{in} + jcn}{C} +\\frac{dG}{dt} = -\\frac{1}{\\tau}G +``` + +where ``jcn`` is any synaptic input to the blox (presumably a current G from another neuron). + +Arguments: +- name: Name given to ODESystem object within the blox. +- namespace: Additional namespace above name if needed for inheritance. +- C: Membrane capicitance (μF). +- Eₘ: Resting membrane potential (mV). +- Rₘ: Membrane resistance (kΩ). +- τ: Synaptic time constant (ms). +- θ: Threshold voltage (mV). +- E_syn: Synaptic reversal potential (mV). +- G_syn: Synaptic conductance (μA/mV). +- I_in: External current input (μA). + +References: +1. Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011). Principles of Computational Modelling in Neuroscience. Cambridge University Press. +""" +# C = [1.0, 10.0] μF +# Eₘ = [-100, -55] mV +# Rₘ = [1, 100] kΩ +# τ = [1.0, 100.0] ms +# θ = [-65, -45] mV +# E_syn = [-100, -55] mV +# G_syn = [0.001, 0.01] μA/mV (bastardized μS - off by factor of 1000) +# I_in = [-2.5, 2.5] μA (you will cook real neurons with these currents) struct LIFNeuron <: AbstractNeuronBlox params output @@ -340,42 +264,36 @@ struct LIFNeuron <: AbstractNeuronBlox namespace=nothing, C=1.0, Eₘ = -70.0, - Rₘ = 100.0, - τ₁=0.1, - τ₂=10.0, - τᵣ=3, + Rₘ = 10.0, + τ = 10.0, θ = -50.0, - E_syn=0, - G_syn=0.2, - I_in=0, - freq=0, - phase=0) - p = paramscoping(C=C, Eₘ=Eₘ, Rₘ=Rₘ, τ₁=τ₁, τ₂=τ₂, τᵣ=τᵣ, θ=θ, E_syn=E_syn, G_syn=G_syn, I_in=I_in, freq=freq, phase=phase) - C, Eₘ, Rₘ, τ₁, τ₂, τᵣ, θ, E_syn, G_syn, I_in, freq, phase = p + E_syn=-70.0, + G_syn=0.002, + I_in=0.0) + p = paramscoping(C=C, Eₘ=Eₘ, Rₘ=Rₘ, τ=τ, θ=θ, E_syn=E_syn, G_syn=G_syn, I_in=I_in) + C, Eₘ, Rₘ, τ, θ, E_syn, G_syn, I_in = p sts = @variables V(t) = -70.00 G(t)=0.0 z(t)=0.0 Cₜ(t) = 0.0 jcn(t)=0.0 [input=true] - eqs = [ D(V) ~ (-(V-Eₘ)/Rₘ + I_in*(sin((t*freq*2*pi/1000)+phase)+1) + jcn)/(C+Cₜ), - D(G)~(-1/τ₂)*G + z, - D(z)~(-1/τ₁)*z, - D(Cₜ)~(-1/τᵣ)*Cₜ + eqs = [ D(V) ~ (-(V-Eₘ)/Rₘ + I_in + jcn)/C, + D(G)~(-1/τ)*G, ] - ev = [V~θ] => [V~Eₘ,z~G_syn,Cₜ~10] + + ev = [V~θ] => [V~Eₘ, G~G+G_syn] sys = ODESystem(eqs, t, sts, p, continuous_events=[ev]; name=name) new(p, sts[2], sts[5], sts[1], sys, namespace) end end # Paramater bounds for GUI -# C = [0.1, 100] -# ω = [0, 100] -# E_syn = [-10, 10] -# G_syn = [0.1, 1] -# τ₁ = [1, 100] -# τ₂ = [1, 100] -# I_in = [-25, 25] -# Eₘ = [-10, 10] -# Vᵣₑₛ = [-100, -55] -# θ = [0, 50] - +# C = [0.1, 100] μF +# E_syn = [1, 100] kΩ +# E_syn = [-10, 10] mV +# G_syn = [0.001, 0.01] μA/mV +# τ₁ = [1, 100] ms +# τ₂ = [1, 100] ms +# I_in = [-2.5, 2.5] μA +# Eₘ = [-10, 10] mV +# Vᵣₑₛ = [-100, -55] mV +# θ = [0, 50] mV struct QIFNeuron <: AbstractNeuronBlox params output @@ -386,19 +304,19 @@ struct QIFNeuron <: AbstractNeuronBlox function QIFNeuron(;name, namespace=nothing, C=1.0, - ω=0.0, + Rₘ = 10.0, E_syn=0.0, - G_syn=1.0, + G_syn=0.002, τ₁=10.0, τ₂=10.0, I_in=0.0, Eₘ=0.0, Vᵣₑₛ=-70.0, θ=25.0) - p = paramscoping(C=C, ω=ω, E_syn=E_syn, G_syn=G_syn, τ₁=τ₁, τ₂=τ₂, I_in=I_in, Eₘ=Eₘ, Vᵣₑₛ=Vᵣₑₛ, θ=θ) - C, ω, E_syn, G_syn, τ₁, τ₂, I_in, Eₘ, Vᵣₑₛ, θ = p + p = paramscoping(C=C, Rₘ=Rₘ, E_syn=E_syn, G_syn=G_syn, τ₁=τ₁, τ₂=τ₂, I_in=I_in, Eₘ=Eₘ, Vᵣₑₛ=Vᵣₑₛ, θ=θ) + C, Rₘ, E_syn, G_syn, τ₁, τ₂, I_in, Eₘ, Vᵣₑₛ, θ = p sts = @variables V(t) = -70.0 G(t)=0.0 z(t)=0.0 jcn(t)=0.0 [input=true] - eqs = [ D(V) ~ ((V-Eₘ)^2+I_in+jcn)/C, + eqs = [ D(V) ~ ((V-Eₘ)^2/(Rₘ^2)+I_in+jcn)/C, D(G)~(-1/τ₂)*G + z, D(z)~(-1/τ₁)*z ] @@ -407,6 +325,7 @@ struct QIFNeuron <: AbstractNeuronBlox new(p, sts[2], sts[4], sts[1], sys, namespace) end end + # Paramater bounds for GUI # α = [0.1, 1] # η = [0, 1] diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl index 74d2370d..05a44dd3 100644 --- a/src/blox/reinforcement_learning.jl +++ b/src/blox/reinforcement_learning.jl @@ -170,12 +170,13 @@ function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem) end """ -mutable struct Agent{S,P,A,LR} +mutable struct Agent{S,P,A,LR,PA} odesystem::S problem::P action_selection::A learning_rules::LR - init_params::Vector{Float64} + init_params::PA + # simsys::SS function Agent(g::MetaDiGraph; name, kwargs...) bc = connector_from_graph(g) @@ -189,14 +190,12 @@ mutable struct Agent{S,P,A,LR} p = haskey(kwargs, :p) ? kwargs[:p] : [] prob = ODEProblem(ss, u0, (0.,1.), p) - init_params = prob.p + init_params = copy(prob.p) policy = action_selection_from_graph(g) learning_rules = bc.learning_rules - - - new{typeof(sys), typeof(prob), typeof(policy), typeof(learning_rules)}(ss, prob, policy, learning_rules, init_params) + new{typeof(sys), typeof(prob), typeof(policy), typeof(learning_rules), typeof(init_params)#=, typeof(ss)=#}(ss, prob, policy, learning_rules, init_params, #=ss=#) end end @@ -234,18 +233,22 @@ function run_experiment!(agent::Agent, env::ClassificationEnvironment, t_warmup= end for _ in Base.OneTo(N_trials) - @show env.current_trial + stim_params = get_trial_stimulus(env) - prob = remake(prob; p = merge(weights, stim_params), u0=u0) + to_update = merge(weights, stim_params) + new_params = ModelingToolkit.MTKParameters(sys, merge(defs, weights, stim_params)) + + prob = remake(prob; p = new_params, u0=u0) if haskey(kwargs, :alg) sol = solve(prob, kwargs[:alg]; kwargs...) else sol = solve(prob; alg_hints = [:stiff], kwargs...) end - #u0 = sol[1:end,end] # next run should continue where the last one ended - # In the paper we assume sufficient time interval before net stimulus so that system reaches back to steady state, so we don't continue from previous trial's endpoint + # u0 = sol[1:end,end] # next run should continue where the last one ended + # In the paper we assume sufficient time interval before net stimulus so that + # system reaches back to steady state, so we don't continue from previous trial's endpoint if isnothing(action_selection) feedback = 1 diff --git a/src/blox/rl_blox.jl b/src/blox/rl_blox.jl index 51f0922a..93017e61 100644 --- a/src/blox/rl_blox.jl +++ b/src/blox/rl_blox.jl @@ -1,6 +1,4 @@ -@parameters t - mutable struct LearningBlox ω::Num d::Num diff --git a/src/blox/sources.jl b/src/blox/sources.jl index 1e692388..7f5dbb6c 100644 --- a/src/blox/sources.jl +++ b/src/blox/sources.jl @@ -1,5 +1,3 @@ -@parameters t - #CosineSource mutable struct CosineSource f::Num diff --git a/src/blox/subcortical_blox.jl b/src/blox/subcortical_blox.jl index 45a38bce..2ed735bd 100644 --- a/src/blox/subcortical_blox.jl +++ b/src/blox/subcortical_blox.jl @@ -59,11 +59,11 @@ struct Striatum <: CompositeBlox end m = if isnothing(namespace) - [s for s in states.((sys,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] else @variables t sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) - [s for s in states.((sys_namespace,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] end new(namespace, parts, sys, bc, m) @@ -125,11 +125,11 @@ struct GPi <: CompositeBlox sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) m = if isnothing(namespace) - [s for s in states.((sys,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] else @variables t sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) - [s for s in states.((sys_namespace,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] end new(namespace, parts, sys, bc, m) @@ -182,11 +182,11 @@ struct GPe <: CompositeBlox sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) m = if isnothing(namespace) - [s for s in states.((sys,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] else @variables t sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) - [s for s in states.((sys_namespace,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] end new(namespace, parts, sys, bc, m) @@ -240,11 +240,11 @@ struct Thalamus <: CompositeBlox sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) m = if isnothing(namespace) - [s for s in states.((sys,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] else @variables t sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) - [s for s in states.((sys_namespace,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] end new(namespace, parts, sys, bc, m) @@ -301,21 +301,21 @@ struct STN <: CompositeBlox # to potentially add more terms to the same connections. sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) - # TO DO : m is a subset of states to be plotted in the GUI. + # TO DO : m is a subset of unknowns to be plotted in the GUI. # This can be moved to NeurobloxGUI, maybe via plotting recipes, # since it is not an essential part of the blox. m = if isnothing(namespace) - [s for s in states.((sys,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] else @variables t - # HACK : Need to define an empty system to add the correct namespace to states. - # Adding a dispatch `ModelingToolkit.states(::Symbol, ::AbstractArray)` upstream will solve this. + # HACK : Need to define an empty system to add the correct namespace to unknowns. + # Adding a dispatch `ModelingToolkit.unknowns(::Symbol, ::AbstractArray)` upstream will solve this. sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) - [s for s in states.((sys_namespace,), states(sys)) if contains(string(s), "V(t)")] + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] end new(namespace, parts, sys, bc, m) end -end \ No newline at end of file +end diff --git a/src/blox/synaptic_network.jl b/src/blox/synaptic_network.jl deleted file mode 100644 index 04a5a042..00000000 --- a/src/blox/synaptic_network.jl +++ /dev/null @@ -1,20 +0,0 @@ -function synaptic_network(;name, sys=sys, adj_matrix=adj_matrix) - syn_eqs = [] - for ii = 1:length(sys) - presyn = findall(x-> x>0, adj_matrix[ii,:]) - wts = adj_matrix[ii,presyn] - presyn_nrn = sys[presyn] - postsyn_nrn = sys[ii] - - if length(presyn)>0 - ind = collect(1:length(presyn)); - push!(syn_eqs, 0 ~ sum(p -> (presyn_nrn[p].E_syn-postsyn_nrn.V)*presyn_nrn[p].G*wts[p], ind) - postsyn_nrn.I_syn) - else - push!(syn_eqs,0 ~ postsyn_nrn.I_syn); - end - end - - @named synaptic_eqs = ODESystem(syn_eqs,t) - @named synaptic_network = compose(synaptic_eqs, sys) - return structural_simplify(synaptic_network) -end diff --git a/src/datafitting/spectralDCM.jl b/src/datafitting/spectralDCM.jl index 6655365c..5b34c45a 100644 --- a/src/datafitting/spectralDCM.jl +++ b/src/datafitting/spectralDCM.jl @@ -30,14 +30,14 @@ mutable struct VLState dFdθθ::Matrix{Float64} # free energy Hessian w.r.t. parameters end -struct VLSetup - model_at_x0 # model evaluated at initial conditions - y_csd::Array{Complex} # cross-spectral density approximated by fitting MARs to data +struct VLSetup{Model, N} + model_at_x0::Model # model evaluated at initial conditions + y_csd::Array{ComplexF64, N} # cross-spectral density approximated by fitting MARs to data tolerance::Float64 # convergence criterion systemnums::Vector{Int} # several integers -> np: n. parameters, ny: n. datapoints, nq: n. Q matrices, nh: n. hyperparameters systemvecs::Vector{Vector{Float64}} # μθ_pr: prior expectation values of parameters and μλ_pr: prior expectation values of hyperparameters systemmatrices::Vector{Matrix{Float64}} # Πθ_pr: prior precision matrix of parameters, Πλ_pr: prior precision matrix of hyperparameters - Q::Matrix{Complex} # linear decomposition of precision matrix of parameters, typically just one matrix, the empirical correlation matrix + Q::Matrix{ComplexF64} # linear decomposition of precision matrix of parameters, typically just one matrix, the empirical correlation matrix end """ @@ -58,7 +58,7 @@ function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np} nd = size(M, 1) A = (p->p.value).(M) F = eigen(A, sortby=nothing, permute=true) - λ, V = F.values, F.vectors + λ, V = F local ∂λ_agg, ∂V_agg # compute eigenvalue and eigenvector derivatives for all partials for i = 1:np @@ -66,7 +66,7 @@ function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np} tmp = V \ dA ∂K = tmp * V # V^-1 * dA * V ∂Kdiag = @view ∂K[diagind(∂K)] - ∂λ_tmp = eltype(λ) <: Real ? real.(∂Kdiag) : copy(∂Kdiag) # why do only copy when complex?? + ∂λ_tmp = eltype(λ) <: Real ? real.(∂Kdiag) : copy(∂Kdiag) # copy only needed for Complex because `real.(v)` makes a new array ∂K ./= transpose(λ) .- λ fill!(∂Kdiag, 0) ∂V_tmp = mul!(tmp, V, ∂K) @@ -79,23 +79,27 @@ function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np} ∂λ_agg = cat(∂λ_agg, ∂λ_tmp, dims=2) end end - ∂V = Array{Partials}(undef, nd, nd) - ∂λ = Array{Partials}(undef, nd) # reassemble the aggregated vectors and values into a Partials type - for i = 1:nd - ∂λ[i] = Partials(Tuple(∂λ_agg[i, :])) - for j = 1:nd - ∂V[i, j] = Partials(Tuple(∂V_agg[i, j, :])) - end + ∂V = map(Iterators.product(1:nd, 1:nd)) do (i, j) + Partials(NTuple{np}(∂V_agg[i, j, :])) + end + ∂λ = map(1:nd) do i + Partials(NTuple{np}(∂λ_agg[i, :])) end if eltype(V) <: Complex - evals = map((x,y)->Complex(Dual{T, Float64, length(y)}(real(x), Partials(Tuple(real(y)))), - Dual{T, Float64, length(y)}(imag(x), Partials(Tuple(imag(y))))), F.values, ∂λ) - evecs = map((x,y)->Complex(Dual{T, Float64, length(y)}(real(x), Partials(Tuple(real(y)))), - Dual{T, Float64, length(y)}(imag(x), Partials(Tuple(imag(y))))), F.vectors, ∂V) + evals = map(λ, ∂λ) do x, y + rex, imx = reim(x) + rey, imy = real.(Tuple(y)), imag.(Tuple(y)) + Complex(Dual{T}(rex, Partials(rey)), Dual{T}(imx, Partials(imy))) + end + evecs = map(V, ∂V) do x, y + rex, imx = reim(x) + rey, imy = real.(Tuple(y)), imag.(Tuple(y)) + Complex(Dual{T}(rex, Partials(rey)), Dual{T}(imx, Partials(imy))) + end else - evals = Dual{T, Float64, length(∂λ[1])}.(F.values, ∂λ) - evecs = Dual{T, Float64, length(∂V[1])}.(F.vectors, ∂V) + evals = Dual{T}.(λ, ∂λ) + evecs = Dual{T}.(V, ∂V) end return Eigen(evals, evecs) end @@ -112,7 +116,7 @@ function transferfunction_fmri(ω, derivatives, params, params_idx) V = F.vectors ∂g∂v = ∂g∂x*V - ∂v∂u = V\∂f∂u # u is external variable which we don't use right now. With external variable this would read V/dfdu + ∂v∂u = V\∂f∂u # u is external variable which we don't use right now. With external variable this would read V/dfdu nω = size(ω, 1) # number of frequencies ng = size(∂g∂x, 1) # number of outputs @@ -123,8 +127,7 @@ function transferfunction_fmri(ω, derivatives, params, params_idx) for i = 1:ng for k = 1:nk # transfer functions (FFT of kernel) - Sk = (1im*2*pi*ω .- Λ[k]).^-1 - S[:,i,j] .+= ∂g∂v[i,k]*∂v∂u[k,j]*Sk + S[:,i,j] .+= (∂g∂v[i,k]*∂v∂u[k,j]) .* ((1im*2*pi) .* ω .- Λ[k]).^-1 end end end @@ -158,7 +161,7 @@ function csd_approx(ω, derivatives, params, params_idx) Gu = zeros(eltype(G), nω, nd, nd) Gn = zeros(eltype(G), nω, nd, nd) for i = 1:nd - Gu[:, i, i] .+= exp(α[1])*G + Gu[:, i, i] .+= exp(α[1]) .* G end # region specific observation noise (1/f or AR(1) form) G = ω.^(-exp(β[2])/2) @@ -214,9 +217,9 @@ end """ function matlab_norm(M, p) if p == 1 - return maximum(vec(sum(abs.(M),dims=1))) + return maximum(sum(abs, M, dims=1)) elseif p == Inf - return maximum(vec(sum(abs.(M),dims=2))) + return maximum(sum(abs, M, dims=2)) elseif p == 2 print("Not implemented yet!\n") return NaN @@ -235,7 +238,7 @@ function csd_Q(csd) end end end - Q = inv(Q .+ matlab_norm(Q, 1)/32*Matrix(I, size(Q))) # TODO: MATLAB's and Julia's norm function are different! Reconciliate? + Q = inv(Q + matlab_norm(Q, 1)*I/32) # TODO: MATLAB's and Julia's norm function are different! Reconciliate? return Q end @@ -250,10 +253,10 @@ end function spm_logdet(M) TOL = 1e-16 s = diag(M) - if sum(abs.(s)) != sum(abs.(M[:])) - ~, s, ~ = svd(M) + if sum(abs, s) != sum(abs, M) + s = svdvals(M) end - return sum(log.(s[(s .> TOL) .& (s .< TOL^-1)])) + return sum((log(sval) for sval in s if TOL < sval < inv(TOL)), init=zero(eltype(s))) end """ @@ -267,7 +270,7 @@ end function vecparam(param::OrderedDict) flatparam = Float64[] for v in values(param) - if (typeof(v) <: Array) + if v isa Array for vv in v push!(flatparam, vv) end @@ -309,7 +312,6 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params p = csdsetup[:p]; # order of MAR mar = mar_ml(Matrix(data), p); # compute MAR from time series y and model order p y_csd = mar2csd(mar, ω, dt^-1); # compute cross spectral densities from MAR parameters at specific frequencies freqs, dt^-1 is sampling rate of data - jac_fg = generate_jacobian(model, expression = Val{false})[1] # compute symbolic jacobian. statevals = [v for v in values(initcond)] @@ -333,7 +335,7 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params 0, # iter -4, # log ascent rate [-Inf], # free energy - [], # delta free energy + Float64[], # delta free energy 8*ones(nh), # metaparameter, initial condition. TODO: why are we not just using the prior mean? zeros(np), # parameter estimation error ϵ_θ [zeros(np), 8*ones(nh)], # memorize reset state @@ -342,7 +344,6 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params zeros(np), zeros(np, np) ) - # variational laplace setup vlsetup = VLSetup( f, # function that computes the cross-spectral density at fixed point 'initcond' @@ -357,13 +358,7 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params end function run_sDCM_iteration!(state::VLState, setup::VLSetup) - μθ_po = state.μθ_po - - λ = state.λ - v = state.v - ϵ_θ = state.ϵ_θ - dFdθ = state.dFdθ - dFdθθ = state.dFdθθ + (;μθ_po, λ, v, ϵ_θ, dFdθ, dFdθθ) = state f = setup.model_at_x0 y = setup.y_csd # cross-spectral density @@ -508,4 +503,4 @@ function run_sDCM_iteration!(state::VLState, setup::VLSetup) append!(state.dF, dF) return state -end \ No newline at end of file +end diff --git a/src/measurementmodels/fmri.jl b/src/measurementmodels/fmri.jl index e25fcd5a..28b29d63 100644 --- a/src/measurementmodels/fmri.jl +++ b/src/measurementmodels/fmri.jl @@ -84,8 +84,57 @@ struct BalloonModel <: ObserverBlox D(lnq) ~ (exp(lnu)/exp(lnq)*((1 - (1 - H[5])^(exp(lnu)^-1))/H[5]) - exp(lnν)^(H[4]^-1 - 1))/(H[3]*exp(lnτ)), bold ~ B[2]*(k1 - k1*exp(lnq) + exp(lnϵ)*B[3]*B[5]*B[1] - exp(lnϵ)*B[3]*B[5]*B[1]*exp(lnq)/exp(lnν) + 1-exp(lnϵ) - (1-exp(lnϵ))*exp(lnν)) ] - - sys = System(eqs, name=name) + sys = System(eqs, t, name=name) new(p, Num(0), sts[5], sys, namespace) end -end \ No newline at end of file +end + + +""" + boldsignal(;name, lnϵ=0.0) + + Bold signal observer function. This requires connection to the variables ν and q of a balloon model. + The formal definition of this blox is: + """ + # ```math + # \\lambda(\\nu, q) = V_0 \\left[ k_1 (1-q) + k_2 \\left( 1 - \\frac{q}{v} \\right) + k_3 (1-v)\\right] + # ``` +""" + +Arguments: +- `name`: Name given to `ODESystem` object within the blox. +- lnϵ : logarithm of ratio of intra- to extra-vascular signal + +NB: the prefix ln of the variables ν, q as well as the parameters ϵ denotes their transformation into logarithmic space +to enforce their positivity. + +Citations: +1. Stephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040 +2. Hofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407 + +""" +function boldsignal(;name, lnϵ=0.0) + # NB: Biophysical constants for 1.5T scanners + # Time to echo + TE = 0.04 + # resting venous volume (%) + V0 = 4 + # slope r0 of intravascular relaxation rate R_iv as a function of oxygen + # saturation S: R_iv = r0*[(1 - S)-(1 - S0)] (Hz) + r0 = 25 + # frequency offset at the outer surface of magnetized vessels (Hz) + nu0 = 40.3 + # resting oxygen extraction fraction + E0 = 0.4 + # Coefficients in BOLD signal model + k1 = 4.3*nu0*E0*TE + + params = @parameters lnϵ=lnϵ + vars = @variables bold(t) lnν(t) lnq(t) # TODO: got to be really careful with the current implementation! A simple permutation of this breaks the algorithm! + + eqs = [ + bold ~ V0*(k1 - k1*exp(lnq) + exp(lnϵ)*r0*E0*TE - exp(lnϵ)*r0*E0*TE*exp(lnq)/exp(lnν) + 1-exp(lnϵ) - (1-exp(lnϵ))*exp(lnν)) + ] + + ODESystem(eqs, t, vars, params; name=name) +end diff --git a/test/components.jl b/test/components.jl index 6972518e..669d1f4f 100644 --- a/test/components.jl +++ b/test/components.jl @@ -40,7 +40,6 @@ end @named osc2 = HarmonicOscillator() params = @parameters k=1.0 - @show typeof(params) adj = [0 k; k 0] g = MetaDiGraph() add_blox!.(Ref(g), [osc1, osc2]) @@ -74,17 +73,17 @@ New Jansen-Rit tests params = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5 adj_matrix_lin = [0 0 0 0 0 0 0 0; - -0.5*C_BG_Th -0.5*C_BG_Th C_BG_Th 0 0 0 0 0; - 0 -0.5*C_BG_Th 0 0 0 0 C_Cor_BG_Th 0; - 0 -0.5*C_BG_Th C_BG_Th 0 0 0 0 0; - 0 0 0 -0.5*C_BG_Th 0 0 0 0; - 0 0 0 0 C_BG_Th_Cor 0 6*C_Cor 0; - 0 0 0 0 0 4.8*C_Cor 0 -1.5*C_Cor; - 0 0 0 0 0 0 1.5*C_Cor 3.3*C_Cor] - - g = MetaDiGraph() - add_blox!.(Ref(g), blox) - create_adjacency_edges!(g, adj_matrix_lin) + -0.5*C_BG_Th -0.5*C_BG_Th C_BG_Th 0 0 0 0 0; + 0 -0.5*C_BG_Th 0 0 0 0 C_Cor_BG_Th 0; + 0 -0.5*C_BG_Th C_BG_Th 0 0 0 0 0; + 0 0 0 -0.5*C_BG_Th 0 0 0 0; + 0 0 0 0 C_BG_Th_Cor 0 6*C_Cor 0; + 0 0 0 0 0 4.8*C_Cor 0 -1.5*C_Cor; + 0 0 0 0 0 0 1.5*C_Cor 3.3*C_Cor] + + g = MetaDiGraph() + add_blox!.(Ref(g), blox) + create_adjacency_edges!(g, adj_matrix_lin) @named final_system = system_from_graph(g, params) final_delays = graph_delays(g) @@ -230,55 +229,12 @@ end @test norm.(R[length(R)]) < 0.1 end -@testset "QIF synaptic network" begin - """ - qif_neuron.jl and synaptic_network.jl test - - This test generates a network of quadratic integrate and fire neurons - using qif_neuron.jl and connects them with synapses using synaptic_network.jl - This should successfully generate a structurally simplified ODESystem for - the entire network. If N is number of neurons and S+1 is the number of state variables - for each neuron (S internal variables and 1 synaptic input), then the total number of states - for the resulting ODESystem for network should be N*S. - """ - - #Generate qif neurons - N_nrn = 10 - nrn_network=[] - - ω₀ = 0.269 - Δω = 0.042 - - for ii = 1:N_nrn - nn = QIFNeuronBlox(name=Symbol("nrn$ii"),C=30.0,E_syn=-10,G_syn=1,ω=rand(Cauchy(ω₀,Δω)),τ=35) - push!(nrn_network,nn.odesystem) - end - - # create synaptic network - k = 0.105 #synaptic weight - adj = ones(N_nrn,N_nrn) - for ii = 1:N_nrn - adj[ii,ii]=0 - end - syn = adj.*k/N_nrn - - @named syn_net = synaptic_network(sys=nrn_network,adj_matrix=syn) - - @test typeof(syn_net) == ODESystem - @test length(states(syn_net)) == 3*N_nrn - - sim_dur = 2.0 - prob = ODEProblem(syn_net, [], (0.0, sim_dur), []) - sol = solve(prob,Rodas5(),saveat=0.01,reltol=1e-4,abstol=1e-4) - @test sol.t[end] == sim_dur -end - @testset "Van der Pol" begin -@named VdP = van_der_pol() - -prob_vdp = SDEProblem(VdP,[0.1,0.1],[0.0, 20.0],[]) -sol = solve(prob_vdp,EM(),dt=0.1) -@test sol.retcode == SciMLBase.ReturnCode.Success + @named VdP = van_der_pol() + + prob_vdp = SDEProblem(complete(VdP),[0.1,0.1],[0.0, 20.0],[]) + sol = solve(prob_vdp,EM(),dt=0.1) + @test sol.retcode == SciMLBase.ReturnCode.Success end """ @@ -288,28 +244,28 @@ Test for OUBlox generator. """ @testset "OUBlox " begin -@named ou1 = OUBlox() -sys = [ou1.odesystem] -eqs = [sys[1].jcn ~ 0.0] -@named ou1connected = compose(System(eqs;name=:connected),sys) -ousimpl = structural_simplify(ou1connected) -prob_ou = SDEProblem(ousimpl,[],(0.0,10.0)) -sol = solve(prob_ou,alg_hints = [:stiff]) -@test sol.retcode == SciMLBase.ReturnCode.Success -@test std(sol[1,:]) > 0.0 # there should be variance + @named ou1 = OUBlox() + sys = [ou1.odesystem] + eqs = [sys[1].jcn ~ 0.0] + @named ou1connected = compose(System(eqs, t; name=:connected),sys) + ousimpl = structural_simplify(ou1connected) + prob_ou = SDEProblem(ousimpl,[],(0.0,10.0)) + sol = solve(prob_ou,alg_hints = [:stiff]) + @test sol.retcode == SciMLBase.ReturnCode.Success + @test std(sol[1,:]) > 0.0 # there should be variance end @testset "OUBlox & Janset-Rit network" begin -@named ou1 = OUBlox() -@named jr = JansenRit() -sys = [ou1.odesystem, jr.odesystem] -eqs = [sys[1].jcn ~ 0.0, sys[2].jcn ~ sys[1].x] -@named ou1connected = compose(System(eqs;name=:connected),sys) -ousimpl = structural_simplify(ou1connected) -prob_oujr = SDEProblem(ousimpl,[],(0.0, 2.0)) -sol = solve(prob_oujr, alg_hints = [:stiff]) -@test sol.retcode == SciMLBase.ReturnCode.Success -@test std(sol[2,:]) > 0.0 # there should be variance + @named ou1 = OUBlox() + @named jr = JansenRit() + sys = [ou1.odesystem, jr.odesystem] + eqs = [sys[1].jcn ~ 0.0, sys[2].jcn ~ sys[1].x] + @named ou1connected = compose(System(eqs, t; name=:connected),sys) + ousimpl = structural_simplify(ou1connected) + prob_oujr = SDEProblem(ousimpl,[],(0.0, 2.0)) + sol = solve(prob_oujr, alg_hints = [:stiff]) + @test sol.retcode == SciMLBase.ReturnCode.Success + @test std(sol[2,:]) > 0.0 # there should be variance end @testset "OUBlox-OUCouplingBlox network" begin @@ -317,7 +273,7 @@ end @named oucp = OUCouplingBlox(μ=2.0, σ=1.0, τ=1.0) sys = [ou1.odesystem, oucp.odesystem] eqs = [sys[1].jcn ~ 0.0, sys[2].jcn ~ sys[1].x] - @named ou1connected = compose(System(eqs;name=:connected),sys) + @named ou1connected = compose(System(eqs, t;name=:connected),sys) ousimpl = structural_simplify(ou1connected) prob_oucp = SDEProblem(ousimpl,[],(0.0,10.0)) sol = solve(prob_oucp) @@ -326,22 +282,22 @@ end end @testset "OUBlox-OUCouplingBlox larger network" begin -@named ou1 = OUBlox(μ=0.0, σ=1.0, τ=3.0) -@named ou2 = OUBlox(μ=0.0, σ=1.0, τ=3.0) -@named oucp1 = OUCouplingBlox(μ=-0.1, σ=0.02, τ=10) -@named oucp2 = OUCouplingBlox(μ=-0.2, σ=0.02, τ=10) -sys = [ou1.odesystem, ou2.odesystem, oucp1.odesystem, oucp2.odesystem] -eqs = [sys[1].jcn ~ oucp1.connector, - sys[2].jcn ~ oucp2.connector, - sys[3].jcn ~ ou2.connector, - sys[4].jcn ~ ou1.connector] -@named ouconnected = compose(System(eqs;name=:connected),sys) -ousimpl = structural_simplify(ouconnected) -prob_ouconnect = SDEProblem(ousimpl,[0,0,-0.1,-0.2],(0.0,100.0)) -sol = solve(prob_ouconnect) -@test sol.retcode == SciMLBase.ReturnCode.Success -@test std(sol[1,:].*sol[2,:]) > 0.0 # there should be variance -@test cor(sol[1,:],sol[2,:]) < 0.2 # Pearson correlation should be negative or small + @named ou1 = OUBlox(μ=0.0, σ=1.0, τ=3.0) + @named ou2 = OUBlox(μ=0.0, σ=1.0, τ=3.0) + @named oucp1 = OUCouplingBlox(μ=-0.1, σ=0.02, τ=10) + @named oucp2 = OUCouplingBlox(μ=-0.2, σ=0.02, τ=10) + sys = [ou1.odesystem, ou2.odesystem, oucp1.odesystem, oucp2.odesystem] + eqs = [sys[1].jcn ~ oucp1.connector, + sys[2].jcn ~ oucp2.connector, + sys[3].jcn ~ ou2.connector, + sys[4].jcn ~ ou1.connector] + @named ouconnected = compose(System(eqs, t; name=:connected), sys) + ousimpl = structural_simplify(ouconnected) + prob_ouconnect = SDEProblem(ousimpl,[0,0,-0.1,-0.2],(0.0,100.0)) + sol = solve(prob_ouconnect) + @test sol.retcode == SciMLBase.ReturnCode.Success + @test std(sol[1,:].*sol[2,:]) > 0.0 # there should be variance + @test cor(sol[1,:],sol[2,:]) < 0.2 # Pearson correlation should be negative or small end # @testset "Time-series output" begin @@ -379,26 +335,26 @@ end # end @testset "HH Neuron excitatory & inhibitory network" begin -nn1 = HHNeuronExciBlox(name=Symbol("nrn1"), I_bg=3, freq=4) -nn2 = HHNeuronExciBlox(name=Symbol("nrn2"), I_bg=2, freq=6) -nn3 = HHNeuronInhibBlox(name=Symbol("nrn3"), I_bg=2, freq=3) -assembly = [nn1, nn2, nn3] - -# Adjacency matrix : -#adj = [0 1 0 -# 0 0 1 -# 0.2 0 0] -g = MetaDiGraph() -add_blox!.(Ref(g), assembly) -add_edge!(g, 1, 2, :weight, 1) -add_edge!(g, 2, 3, :weight, 1) -add_edge!(g, 3, 1, :weight, 0.2) - -@named neuron_net = system_from_graph(g) -prob = ODEProblem(structural_simplify(neuron_net), [], (0.0, 2), []) -sol = solve(prob, Vern7()) -@test neuron_net isa ODESystem -@test sol.retcode == ReturnCode.Success + nn1 = HHNeuronExciBlox(name=Symbol("nrn1"), I_bg=3, freq=4) + nn2 = HHNeuronExciBlox(name=Symbol("nrn2"), I_bg=2, freq=6) + nn3 = HHNeuronInhibBlox(name=Symbol("nrn3"), I_bg=2, freq=3) + assembly = [nn1, nn2, nn3] + + # Adjacency matrix : + #adj = [0 1 0 + # 0 0 1 + # 0.2 0 0] + g = MetaDiGraph() + add_blox!.(Ref(g), assembly) + add_edge!(g, 1, 2, :weight, 1) + add_edge!(g, 2, 3, :weight, 1) + add_edge!(g, 3, 1, :weight, 0.2) + + @named neuron_net = system_from_graph(g) + prob = ODEProblem(structural_simplify(neuron_net), [], (0.0, 2), []) + sol = solve(prob, Vern7()) + @test neuron_net isa ODESystem + @test sol.retcode == ReturnCode.Success end @testset "NextGenerationEIBlox connected to neuron" begin @@ -559,26 +515,26 @@ end end @testset "IF Neuron Network" begin - @named if1 = IFNeuron(I_in=3.0) - @named if2 = IFNeuron(I_in=1.0) + @named if1 = IFNeuron(I_in=2.5) + @named if2 = IFNeuron(I_in=1.5) g = MetaDiGraph() add_blox!.(Ref(g), [if1, if2]) - add_edge!(g, 1, 2, Dict(:weight => -0.5, :connection_rule => "basic")) - add_edge!(g, 2, 1, Dict(:weight => -1.0, :connection_rule => "basic")) + add_edge!(g, 1, 2, Dict(:weight => -0.008, :connection_rule => "basic")) + add_edge!(g, 2, 1, Dict(:weight => -0.007, :connection_rule => "basic")) @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) - prob = ODEProblem(sys_simpl, [], (0, 10.0)) + prob = ODEProblem(sys_simpl, [], (0, 100.0)) sol = solve(prob) @test sol.retcode == ReturnCode.Success end @testset "LIF Neuron Network" begin - @named lif1 = LIFNeuron(I_in=1.0) - @named lif2 = LIFNeuron(I_in=0.1) + @named lif1 = LIFNeuron(I_in=2.2) + @named lif2 = LIFNeuron(I_in=2.1) g = MetaDiGraph() add_blox!.(Ref(g), [lif1, lif2]) add_edge!(g, 1, 2, Dict(:weight => 1.0, :connection_rule => "psp")) - add_edge!(g, 2, 1, Dict(:weight => 0.5, :connection_rule => "psp")) + add_edge!(g, 2, 1, Dict(:weight => 1.0, :connection_rule => "psp")) @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 200.0)) @@ -587,7 +543,7 @@ end end @testset "QIF Neuron Network" begin - @named qif1 = QIFNeuron(I_in=3.0) + @named qif1 = QIFNeuron(I_in=2.5) @named qif2 = QIFNeuron(I_in=1.0) g = MetaDiGraph() add_blox!.(Ref(g), [qif1, qif2]) @@ -595,7 +551,7 @@ end add_edge!(g, 2, 1, Dict(:weight => 1.0, :connection_rule => "psp")) @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) - prob = ODEProblem(sys_simpl, [], (0, 50.0)) + prob = ODEProblem(sys_simpl, [], (0, 200.0)) sol = solve(prob) @test sol.retcode == ReturnCode.Success end @@ -612,4 +568,15 @@ end prob = ODEProblem(sys_simpl, [], (0, 200.0)) sol = solve(prob) @test sol.retcode == ReturnCode.Success -end \ No newline at end of file +end + +@testset "Single Block" begin + @named solo = JansenRit() + g = MetaDiGraph() + add_blox!(g, solo) + @named sys = system_from_graph(g) + sys_simpl = structural_simplify(sys) + prob = ODEProblem(sys_simpl, [], (0, 200.0)) + sol = solve(prob) + @test sol.retcode == ReturnCode.Success +end diff --git a/test/datafitting.jl b/test/datafitting.jl index f2e3b0e3..e3a9d5df 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -35,11 +35,12 @@ end # compose model @named neuronmodel = system_from_graph(g) -neuronmodel = structural_simplify(neuronmodel) +neuronmodel = structural_simplify(neuronmodel; split=false) # attribute initial conditions to states ds_states, idx_u, idx_bold = get_dynamic_states(neuronmodel) initcond = OrderedDict(ds_states .=> 0.0) + rnames = [] map(x->push!(rnames, split(string(x), "₊")[1]), ds_states); rnames = unique(rnames); @@ -88,11 +89,11 @@ for (k, v) in paramvariance end priors = DataFrame(name=[k for k in keys(modelparam)], mean=[m for m in values(modelparam)], variance=[v for v in values(paramvariance)]) -hyperpriors = Dict(:Πλ_pr => vars["ihC"]*ones(1, 1), # prior metaparameter precision, needs to be a matrix - :μλ_pr => [vars["hE"]] # prior metaparameter mean, needs to be a vector - ); +hyperpriors = (Πλ_pr = vars["ihC"]*ones(1, 1), # prior metaparameter precision, needs to be a matrix + μλ_pr = [vars["hE"]] # prior metaparameter mean, needs to be a vector + ); -csdsetup = Dict(:p => 8, :freq => vec(vars["Hz"]), :dt => vars["dt"]); +csdsetup = (p = 8, freq = vec(vars["Hz"]), dt = vars["dt"]); (state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, params_idx); for iter in 1:128 @@ -112,4 +113,4 @@ print("maxixmum iterations reached\n") ### COMPARE RESULTS WITH MATLAB RESULTS ### @show state.F[end], vars["F"] @test state.F[end] < vars["F"]*0.99 -@test state.F[end] > vars["F"]*1.01 \ No newline at end of file +@test state.F[end] > vars["F"]*1.01 diff --git a/test/final_dde_before_merge.jl b/test/final_dde_before_merge.jl index 49513197..77b65e95 100644 --- a/test/final_dde_before_merge.jl +++ b/test/final_dde_before_merge.jl @@ -54,7 +54,7 @@ function get_namespaced_sys(blox) ODESystem( equations(sys), independent_variable(sys), - states(sys), + unknowns(sys), parameters(sys); name = namespaced_name(inner_namespaceof(blox), nameof(blox)) ) @@ -234,4 +234,4 @@ sol_mtk = solve(prob, alg, reltol = 1e-7, abstol = 1e-10, saveat=0.001) # Review changes (namespacing with iv = t) # Review changes (delay) # Multiple dispatch or separate bloxs for delay? -# Where to collect lags? \ No newline at end of file +# Where to collect lags? diff --git a/test/plasticity.jl b/test/plasticity.jl index d607f842..142789fb 100644 --- a/test/plasticity.jl +++ b/test/plasticity.jl @@ -5,6 +5,7 @@ using Graphs using MetaGraphs using DataFrames using CSV +using ModelingToolkit: getp @testset "Cortical-Cortical plasticity test" begin t_trial = 100 # ms @@ -40,12 +41,19 @@ using CSV idx_jcn = findall(x -> occursin("jcn", String(Symbol(x))), ps) idxs_other_params = setdiff(eachindex(ps), vcat(idxs_weight, idx_stim, idx_jcn)) + + params_at(idxs) = getp(agent.problem, parameters(agent.odesystem)[idxs])(agent.problem) + init_params_all = params_at(:) + init_params_idxs_weight = params_at(idxs_weight) + init_params_idxs_other_params = params_at(idxs_other_params) + + env = ClassificationEnvironment(stim; name=:env, namespace=global_ns) run_experiment!(agent, env; alg=Tsit5(), reltol=1e-6,abstol=1e-9) final_params = agent.problem.p # At least some weights need to be different. - @test any(init_params[map_idxs[idxs_weight]] .!= final_params[map_idxs[idxs_weight]]) + @test any(init_params_idxs_weight .!= params_at(idxs_weight)) # All non-weight parameters need to be the same. - @test all(init_params[map_idxs[idxs_other_params]] .== final_params[map_idxs[idxs_other_params]]) + @test all(init_params_idxs_other_params .== params_at(idxs_other_params)) end diff --git a/test/reinforcement_learning.jl b/test/reinforcement_learning.jl index 39fee2eb..59612420 100644 --- a/test/reinforcement_learning.jl +++ b/test/reinforcement_learning.jl @@ -5,6 +5,7 @@ using Graphs using MetaGraphs using DataFrames using CSV +using ModelingToolkit: getp @testset "RL test" begin t_trial = 2 # ms @@ -51,7 +52,8 @@ using CSV agent = Agent(g; name=:ag, t_block = t_trial/5); ps = parameters(agent.odesystem) - init_params = agent.problem.p + + map_idxs = Int.(ModelingToolkit.varmap_to_vars([ps[i] => i for i in eachindex(ps)], ps)) idxs_weight = findall(x -> occursin("w_", String(Symbol(x))), ps) idx_stim = findall(x -> occursin("stim₊", String(Symbol(x))), ps) @@ -61,18 +63,25 @@ using CSV idx_I_bg = findall(x -> occursin("I_bg", String(Symbol(x))), ps) idxs_other_params = setdiff(eachindex(ps), vcat(idxs_weight, idx_stim, idx_jcn, idx_spikes, idx_H, idx_I_bg)) + params_at(idxs) = getp(agent.problem, parameters(agent.odesystem)[idxs])(agent.problem) + init_params_all = params_at(:) + init_params_idxs_weight = params_at(idxs_weight) + init_params_idxs_other_params = params_at(idxs_other_params) + env = ClassificationEnvironment(stim; name=:env, namespace=global_ns) run_experiment!(agent, env; alg=Vern7(), reltol=1e-9,abstol=1e-9) - - final_params = agent.problem.p + + final_params = reduce(vcat, agent.problem.p) # At least some weights need to be different. - @test any(init_params[map_idxs[idxs_weight]] .!= final_params[map_idxs[idxs_weight]]) + @test any(init_params_idxs_weight .!= params_at(idxs_weight)) + # @test any(init_params[map_idxs[idxs_weight]] .!= final_params[map_idxs[idxs_weight]]) # All non-weight parameters need to be the same. - @test all(init_params[map_idxs[idxs_other_params]] .== final_params[map_idxs[idxs_other_params]]) + @test all(init_params_idxs_other_params .== params_at(idxs_other_params)) + # @test all(init_params[map_idxs[idxs_other_params]] .== final_params[map_idxs[idxs_other_params]]) reset!(agent) - @test all(init_params .== agent.problem.p) - + @test all(init_params_all .== params_at(:)) + @show setdiff(init_params_all, params_at(:)) reset!(env) @test env.current_trial == 1 end diff --git a/test/runtests.jl b/test/runtests.jl index 7bab03ca..9d8fb215 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -8,6 +8,7 @@ using SafeTestsets # @time @safetestset "Neural Signal Measurement Models Tests" begin include("measurementmodels.jl") end # @time @safetestset "Spectral Utilities Tests" begin include("spectraltools.jl") end + @time @safetestset "Data Fitting Tests" begin include("datafitting.jl") end #@time @safetestset "ODE from Graph and simulate" begin include("graph_to_dataframe.jl") end #This no longer works because it's built on the old blocks @time @safetestset "Learning Tests" begin include("learning.jl") end @@ -22,4 +23,4 @@ using SafeTestsets # Extra tests illustrating use - could be removed once examples are written #@time @safetestset "New Jansen-Rit Tests" begin include("jansen_rit_component_tests.jl") end -#@time @safetestset "New Larter-Breakspear Tests" begin include("new_LB_blox.jl") end \ No newline at end of file +#@time @safetestset "New Larter-Breakspear Tests" begin include("new_LB_blox.jl") end diff --git a/test/source_components.jl b/test/source_components.jl index 8df50187..d05a3384 100644 --- a/test/source_components.jl +++ b/test/source_components.jl @@ -4,7 +4,7 @@ using Neuroblox, OrdinaryDiffEq, Statistics CosineSource Test """ # Compare CosineBlox (_nb) to Cosine from MTK Standard Library -@parameters t + @named int = Integrator() # CosineBlox @named src_nb = CosineSource(f=1, a=2, phi=0, offset=1, tstart=2) @@ -19,4 +19,4 @@ prob_nb = ODEProblem(sys_nb, Pair[int.x => 0.0], (0.0, 10.0)) prob = ODEProblem(sys, Pair[int.x => 0.0], (0.0, 10.0)) sol_nb = solve(prob_nb, Rodas4()) sol = solve(prob, Rodas4()) -@test mean(sol_nb[1,:]) ≈ mean(sol[1,:]) \ No newline at end of file +@test mean(sol_nb[1,:]) ≈ mean(sol[1,:]) From 0ff7e4d1dd64a9f89fab2fba2ba80e7d05bbefa7 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Thu, 16 May 2024 16:11:15 -0400 Subject: [PATCH 06/25] add new neurons --- src/Neuroblox.jl | 3 +- src/blox/neuron_models.jl | 337 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 339 insertions(+), 1 deletion(-) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 98f1715a..085a8e1d 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -186,7 +186,8 @@ end export JansenRitSPM12, next_generation, qif_neuron, if_neuron, hh_neuron_excitatory, hh_neuron_inhibitory, van_der_pol export HHNeuronExciBlox, HHNeuronInhibBlox, IFNeuron, LIFNeuron, QIFNeuron, IzhikevichNeuron, - CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical + CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, + HHNeuronInhib_GPe_Adam_Blox export LinearNeuralMass, HarmonicOscillator, JansenRit, WilsonCowan, LarterBreakspear, NextGenerationBlox, NextGenerationResolvedBlox, NextGenerationEIBlox export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc export HebbianPlasticity, HebbianModulationPlasticity diff --git a/src/blox/neuron_models.jl b/src/blox/neuron_models.jl index 0eba160b..99c55e28 100644 --- a/src/blox/neuron_models.jl +++ b/src/blox/neuron_models.jl @@ -165,6 +165,343 @@ struct HHNeuronInhibBlox <: AbstractInhNeuronBlox end end +#These neurons were used in Adam et. al 2022 model for DBS + +struct HHNeuronInhib_MSN_Adam_Blox <: AbstractInhNeuronBlox + odesystem + namespace + + function HHNeuronInhib_MSN_Adam_Blox(; + name, + namespace=nothing, + E_syn=-80.0, + I_bg=1.19, + freq=0, + phase=0, + τ=13, + Cₘ=1.0, + σ=4.0, + a=2, + b=4, + T=37 + ) + sts = @variables begin + V(t)=-67.00 + n(t)=0.32 + m(t)=0.05 + h(t)=0.59 + mM(t)=0.32 + I_syn(t)=0.0 + [input=true] + I_in(t)=0.0 + [input=true] + I_asc(t)=0.0 + [input=true] + G(t)=0.0 + end + + ps = @parameters begin + E_syn=E_syn + G_Na = 100 + G_K = 80 + G_L = 0.1 + G_M = 1.3 + E_Na = 50 + E_K = -100 + E_L = -67 + I_bg=I_bg + freq = freq + phase = phase + Cₘ = Cₘ + σ = σ + a = a + b = b + T = T + end + + @brownian χ + + αₙ(v) = 0.032*(v+52)/(1-exp(-(v+52)/5)) + βₙ(v) = 0.5*exp(-(v+57)/40) + αₘ(v) = 0.32*(v+54)/(1-exp(-(v+54)/4)) + βₘ(v) = 0.28*(v+27)/(exp((v+27)/5)-1) + αₕ(v) = 0.128*exp(-(v+50)/18) + βₕ(v) = 4/(1+exp(-(v+27)/5)) + αₘₘ(v) = Q(T)*10^(-4)*(v+30)/(1-exp(-(v+30)/9)) + βₘₘ(v) = -Q(T)*10^(-4)*(v+30)/(1-exp((v+30)/9)) + + Q(T) = 2.3^((T-23)/10) + + G_asymp(v,a,b) = a*(1+tanh(v/b)) + + eqs = [ + D(V)~(1/Cₘ)*(-G_Na*m^3*h*(V-E_Na)-G_K*n^4*(V-E_K)-G_L*(V-E_L)-G_M*mM*(V-E_K)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_asc+I_in+σ*χ), + D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), + D(m)~(αₘ(V)*(1-m)-βₘ(V)*m), + D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), + D(mM)~(αₘₘ(V)*(1-mM)-βₘₘ(V)*mM), + D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G) + + ] + + sys = System( + eqs, t, sts, ps; + name = Symbol(name) + ) + + new(sys, namespace) + end +end + +struct HHNeuronInhib_FSI_Adam_Blox <: AbstractInhNeuronBlox + odesystem + namespace + + function HHNeuronInhib_FSI_Adam_Blox(; + name, + namespace=nothing, + E_syn=-80.0, + I_bg=5.5, + freq=0, + phase=0, + τ=11, + τₛ=6.5, + Cₘ=1.0, + σ=60.0, + a=4, + b=10, + T=37 + ) + sts = @variables begin + V(t)=-70.00 + n(t)=0.32 + h(t)=0.59 + mD(t)=0.05 + hD(t)=0.59 + I_syn(t)=0.0 + [input=true] + I_in(t)=0.0 + [input=true] + I_asc(t)=0.0 + [input=true] + G(t)=0.0 + Gₛ(t)=0.0 + end + + ps = @parameters begin + E_syn=E_syn + G_Na = 100 + G_K = 80 + G_L = 0.25 + G_D = 6 + E_Na = 50 + E_K = -90 + E_L = -70 + I_bg=I_bg + freq = freq + phase = phase + Cₘ = Cₘ + σ = σ + a = a + b = b + T = T + end + + @brownian χ + + n_inf(v) = 1/(1+exp(-(v+12.4)/6.8)) + τₙ(v) = (0.087+11.4/(1+exp((v+14.6)/8.6)))*(0.087+11.4/(1+exp(-(v-1.3)/18.7))) + m_inf(v) = 1/(1+exp(-(v+24)/11.5)) + h_inf(v) = 1/(1+exp((v+58.3)/6.7)) + τₕ(v) = 0.5 + 14/(1+exp((v+60)/12)) + mD_inf(v) = 1/(1+exp(-(v+50)/20)) + τₘD(v) = 2 + hD_inf(v) = 1/(1+exp((v+70)/6)) + τₕD(v) = 150 + G_asymp(v,a,b) = a*(1+tanh(v/b)) + + eqs = [ + D(V)~(1/Cₘ)*(-G_Na*m_inf(V)^3*h*(V-E_Na)-G_K*n^2*(V-E_K)-G_L*(V-E_L)-G_D*mD^3*hD*(V-E_K)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_asc+I_in+σ*χ), + D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), + D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), + D(mD)~(mD_inf(V)-mD)/τₘD(V), + D(hD)~(hD_inf(V)-hD)/τₕD(V), + D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G), + D(Gₛ)~(-1/τₛ)*Gₛ + G_asymp(V,a,b)*(1-Gₛ) + + ] + + sys = System( + eqs, t, sts, ps; + name = Symbol(name) + ) + + new(sys, namespace) + end +end + +struct HHNeuronExci_STN_Adam_Blox <: AbstractExciNeuronBlox + odesystem + namespace + + function HHNeuronExci_STN_Adam_Blox(; + name, + namespace=nothing, + E_syn=0.0, + I_bg=1.9, + freq=0, + phase=0, + τ=2, + Cₘ=1.0, + σ=80.0, + a=5, + b=4 + ) + sts = @variables begin + V(t)=-67.00 + n(t)=0.32 + m(t)=0.05 + h(t)=0.59 + I_syn(t)=0.0 + [input=true] + I_in(t)=0.0 + [input=true] + I_asc(t)=0.0 + [input=true] + G(t)=0.0 + end + + ps = @parameters begin + E_syn=E_syn + G_Na = 100 + G_K = 80 + G_L = 0.1 + E_Na = 50 + E_K = -100 + E_L = -67 + I_bg=I_bg + freq = freq + phase = phase + Cₘ = Cₘ + σ = σ + a = a + b = b + end + + @brownian χ + + αₙ(v) = 0.032*(v+52)/(1-exp(-(v+52)/5)) + βₙ(v) = 0.5*exp(-(v+57)/40) + αₘ(v) = 0.32*(v+54)/(1-exp(-(v+54)/4)) + βₘ(v) = 0.28*(v+27)/(exp((v+27)/5)-1) + αₕ(v) = 0.128*exp(-(v+50)/18) + βₕ(v) = 4/(1+exp(-(v+27)/5)) + + G_asymp(v,a,b) = a*(1+tanh(v/b)) + + eqs = [ + D(V)~(1/Cₘ)*(-G_Na*m^3*h*(V-E_Na)-G_K*n^4*(V-E_K)-G_L*(V-E_L)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_asc+I_in+σ*χ), + D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), + D(m)~(αₘ(V)*(1-m)-βₘ(V)*m), + D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), + D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G) + + ] + + sys = System( + eqs, t, sts, ps; + name = Symbol(name) + ) + + new(sys, namespace) + end +end + +struct HHNeuronInhib_GPe_Adam_Blox <: AbstractInhNeuronBlox + odesystem + namespace + + function HHNeuronInhib_GPe_Adam_Blox(; + name, + namespace=nothing, + E_syn=-80.0, + I_bg=3.0, + freq=0, + phase=0, + τ=10, + Cₘ=1.0, + σ=80.0, + a=2, + b=4, + T=37 + ) + sts = @variables begin + V(t)=-67.00 + n(t)=0.32 + m(t)=0.05 + h(t)=0.59 + mM(t)=0.32 + I_syn(t)=0.0 + [input=true] + I_in(t)=0.0 + [input=true] + I_asc(t)=0.0 + [input=true] + G(t)=0.0 + end + + ps = @parameters begin + E_syn=E_syn + G_Na = 100 + G_K = 80 + G_L = 0.1 + G_M = 1.3 + E_Na = 50 + E_K = -100 + E_L = -67 + I_bg=I_bg + freq = freq + phase = phase + Cₘ = Cₘ + σ = σ + a = a + b = b + T = T + end + + @brownian χ + + αₙ(v) = 0.032*(v+52)/(1-exp(-(v+52)/5)) + βₙ(v) = 0.5*exp(-(v+57)/40) + αₘ(v) = 0.32*(v+54)/(1-exp(-(v+54)/4)) + βₘ(v) = 0.28*(v+27)/(exp((v+27)/5)-1) + αₕ(v) = 0.128*exp(-(v+50)/18) + βₕ(v) = 4/(1+exp(-(v+27)/5)) + + + G_asymp(v,a,b) = a*(1+tanh(v/b)) + + eqs = [ + D(V)~(1/Cₘ)*(-G_Na*m^3*h*(V-E_Na)-G_K*n^4*(V-E_K)-G_L*(V-E_L)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_asc+I_in+σ*χ), + D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), + D(m)~(αₘ(V)*(1-m)-βₘ(V)*m), + D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), + D(mM)~(αₘₘ(V)*(1-mM)-βₘₘ(V)*mM), + D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G) + + ] + + sys = System( + eqs, t, sts, ps; + name = Symbol(name) + ) + + new(sys, namespace) + end +end + + + """ IFNeuron(name, namespace, C, θ, Eₘ, I_in) From cdb843a5a879e976f3f9000b5cb0befbf0ce5a5f Mon Sep 17 00:00:00 2001 From: harisorgn Date: Wed, 22 May 2024 12:21:00 +0300 Subject: [PATCH 07/25] add a `H_learning` state in `Striatum` parts that tracts the `H` parameter --- src/blox/discrete.jl | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/src/blox/discrete.jl b/src/blox/discrete.jl index 14597018..2e8d914f 100644 --- a/src/blox/discrete.jl +++ b/src/blox/discrete.jl @@ -9,12 +9,16 @@ struct Matrisome <: AbstractDiscrete function Matrisome(; name, namespace=nothing, t_event=180.0) #HACK : this t_event has to be informed from the t_event in Action Selection block @variables t - sts = @variables ρ(t)=0.0 ρ_(t) + # HACK: H_learning is a state version of the H parameter. + # It will be simplified away, but we need to access its value in the solution object + # to calculate the weight_gradient for reinforcement learning after the simulation. + sts = @variables ρ(t)=1.0 ρ_(t)=1.0 H_learning(t)=1.0 #HACK : jcn_ and H_ store the value of jcn and H at time t_event that can be accessed after the simulation ps = @parameters H=1 TAN_spikes=0.0 jcn=0.0 [input=true] jcn_=0.0 H_=1 eqs = [ ρ ~ H*jcn, - ρ_ ~ H_*jcn_ + ρ_ ~ H_*jcn_, + H_learning ~ H ] cb_eqs = [ jcn_ ~ jcn, H_ ~ H @@ -32,10 +36,14 @@ struct Striosome <: AbstractDiscrete function Striosome(; name, namespace=nothing) @variables t - sts = @variables ρ(t)=0.0 + # HACK: H_learning is a state version of the H parameter. + # It will be simplified away, but we need to access its value in the solution object + # to calculate the weight_gradient for reinforcement learning after the simulation. + sts = @variables ρ(t)=0.0 H_learning(t)=1.0 ps = @parameters H=1 jcn=0.0 [input=true] eqs = [ ρ ~ H*jcn, + H_learning ~ H ] sys = ODESystem(eqs, t, sts, ps; name) From 69ebaa842519db475da138023787f55a51bc0f07 Mon Sep 17 00:00:00 2001 From: harisorgn Date: Wed, 22 May 2024 12:21:40 +0300 Subject: [PATCH 08/25] use `H_learning` state instead of `H` param for `Cortical-Striatum` weight learning --- src/blox/connections.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blox/connections.jl b/src/blox/connections.jl index 19aeb84c..76210e31 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -415,7 +415,7 @@ function (bc::BloxConnector)( if haskey(kwargs, :learning_rule) lr = deepcopy(kwargs[:learning_rule]) sys_matr = get_namespaced_sys(get_matrisome(str)) - maybe_set_state_post!(lr, sys_matr.H) + maybe_set_state_post!(lr, sys_matr.H_learning) kwargs = (kwargs..., learning_rule=lr) end @@ -473,7 +473,7 @@ function (bc::BloxConnector)( if haskey(kwargs, :learning_rule) lr = deepcopy(kwargs[:learning_rule]) maybe_set_state_pre!(lr, sys_out.spikes_cumulative) - maybe_set_state_post!(lr, sys_in.H) + maybe_set_state_post!(lr, sys_in.H_learning) bc.learning_rules[w] = lr end From 67c19c3e0a91a166b51aeb6bddc8fb1f2c7fa3c4 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Wed, 22 May 2024 17:58:21 -0400 Subject: [PATCH 09/25] add gap junction connections --- Project.toml | 1 + src/blox/blox_utilities.jl | 12 ++++++++++ src/blox/connections.jl | 31 ++++++++++++++++++++++--- src/blox/neuron_models.jl | 46 +++++++++++++++++++++----------------- 4 files changed, 66 insertions(+), 24 deletions(-) diff --git a/Project.toml b/Project.toml index 29cc312a..35acaae8 100644 --- a/Project.toml +++ b/Project.toml @@ -40,6 +40,7 @@ OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1" OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" Peaks = "18e31ff7-3703-566c-8e60-38913d67486b" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" Reexport = "189a3867-3050-52da-a836-e630ba90ab69" diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl index 8de8b461..2550e632 100644 --- a/src/blox/blox_utilities.jl +++ b/src/blox/blox_utilities.jl @@ -168,6 +168,14 @@ function get_weight(kwargs, name_blox1, name_blox2) end end +function get_gap_weight(kwargs, name_blox1, name_blox2) + if haskey(kwargs, :gap_weight) + return kwargs[:gap_weight] + else + error("Gap junction weight from $name_blox1 to $name_blox2 is not specified.") + end +end + function get_weightmatrix(kwargs, name_blox1, name_blox2) if haskey(kwargs, :weightmatrix) return kwargs[:weightmatrix] @@ -197,6 +205,10 @@ function get_sta(kwargs, name_blox1, name_blox2) haskey(kwargs, :sta) ? kwargs[:sta] : false end +function get_gap(kwargs, name_blox1, name_blox2) + haskey(kwargs, :gap) ? kwargs[:gap] : false +end + function get_event_time(kwargs, name_blox1, name_blox2) if haskey(kwargs, :t_event) return kwargs[:t_event] diff --git a/src/blox/connections.jl b/src/blox/connections.jl index 76210e31..aaf529c4 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -80,6 +80,21 @@ function generate_weight_param(blox_out, blox_in; kwargs...) return w end +function generate_gap_weight_param(blox_out, blox_in; kwargs...) + name_out = namespaced_nameof(blox_out) + name_in = namespaced_nameof(blox_in) + + gap_weight = get_gap_weight(kwargs, name_out, name_in) + gw_name = Symbol("g_w_$(name_out)_$(name_in)") + if typeof(gap_weight) == Num # Symbol + gw = gap_weight + else + gw = only(@parameters $(gw_name)=gap_weight) + end + + return gw +end + function hypergeometric_connections!(bc, neurons_out, neurons_in, name_out, name_in; kwargs...) density = get_density(kwargs, name_out, name_in) N_connects = density * length(neurons_in) * length(neurons_out) @@ -121,8 +136,8 @@ function params(bc::BloxConnector) end function (bc::BloxConnector)( - HH_out::Union{HHNeuronExciBlox, HHNeuronInhibBlox}, - HH_in::Union{HHNeuronExciBlox, HHNeuronInhibBlox}; + HH_out::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}, + HH_in::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}; kwargs... ) sys_out = get_namespaced_sys(HH_out) @@ -144,8 +159,18 @@ function (bc::BloxConnector)( else sys_in.I_syn ~ -w * sys_out.G * (sys_in.V - sys_out.E_syn) end - + accumulate_equation!(bc, eq) + + GAP = get_gap(kwargs, nameof(HH_out), nameof(HH_in)) + if GAP + w_gap = generate_gap_weight_param(HH_out, HH_in; kwargs...) + push!(bc.weights, w_gap) + eq2 = sys_in.I_gap ~ -w_gap * (sys_in.V - sys_out.V) + accumulate_equation!(bc, eq2) + eq3 = sys_out.I_gap ~ -w_gap * (sys_out.V - sys_in.V) + accumulate_equation!(bc, eq3) + end end function (bc::BloxConnector)( diff --git a/src/blox/neuron_models.jl b/src/blox/neuron_models.jl index 99c55e28..536bce66 100644 --- a/src/blox/neuron_models.jl +++ b/src/blox/neuron_models.jl @@ -186,11 +186,11 @@ struct HHNeuronInhib_MSN_Adam_Blox <: AbstractInhNeuronBlox T=37 ) sts = @variables begin - V(t)=-67.00 - n(t)=0.32 - m(t)=0.05 - h(t)=0.59 - mM(t)=0.32 + V(t)=-63.83 + n(t)=0.062 + m(t)=0.027 + h(t)=0.99 + mM(t)=0.022 I_syn(t)=0.0 [input=true] I_in(t)=0.0 @@ -198,6 +198,7 @@ struct HHNeuronInhib_MSN_Adam_Blox <: AbstractInhNeuronBlox I_asc(t)=0.0 [input=true] G(t)=0.0 + [output = true] end ps = @parameters begin @@ -274,24 +275,28 @@ struct HHNeuronInhib_FSI_Adam_Blox <: AbstractInhNeuronBlox ) sts = @variables begin V(t)=-70.00 - n(t)=0.32 - h(t)=0.59 + n(t)=0.032 + h(t)=0.059 mD(t)=0.05 - hD(t)=0.59 + hD(t)=0.059 I_syn(t)=0.0 [input=true] + I_gap(t)=0.0 + [input=true] I_in(t)=0.0 [input=true] I_asc(t)=0.0 [input=true] G(t)=0.0 + [output = true] Gₛ(t)=0.0 + [output = true] end ps = @parameters begin E_syn=E_syn - G_Na = 100 - G_K = 80 + G_Na = 112.5 + G_K = 225 G_L = 0.25 G_D = 6 E_Na = 50 @@ -321,9 +326,9 @@ struct HHNeuronInhib_FSI_Adam_Blox <: AbstractInhNeuronBlox G_asymp(v,a,b) = a*(1+tanh(v/b)) eqs = [ - D(V)~(1/Cₘ)*(-G_Na*m_inf(V)^3*h*(V-E_Na)-G_K*n^2*(V-E_K)-G_L*(V-E_L)-G_D*mD^3*hD*(V-E_K)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_asc+I_in+σ*χ), - D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), - D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), + D(V)~(1/Cₘ)*(-G_Na*m_inf(V)^3*h*(V-E_Na)-G_K*n^2*(V-E_K)-G_L*(V-E_L)-G_D*mD^3*hD*(V-E_K)+I_bg*(sin(t*freq*2*pi/1000)+1)+I_syn+I_gap+I_asc+I_in+σ*χ), + D(n)~(n_inf(V)-n)/τₙ(V), + D(h)~(h_inf(V)-h)/τₕ(V), D(mD)~(mD_inf(V)-mD)/τₘD(V), D(hD)~(hD_inf(V)-hD)/τₕD(V), D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G), @@ -359,9 +364,9 @@ struct HHNeuronExci_STN_Adam_Blox <: AbstractExciNeuronBlox ) sts = @variables begin V(t)=-67.00 - n(t)=0.32 + n(t)=0.032 m(t)=0.05 - h(t)=0.59 + h(t)=0.059 I_syn(t)=0.0 [input=true] I_in(t)=0.0 @@ -369,6 +374,7 @@ struct HHNeuronExci_STN_Adam_Blox <: AbstractExciNeuronBlox I_asc(t)=0.0 [input=true] G(t)=0.0 + [output = true] end ps = @parameters begin @@ -437,10 +443,9 @@ struct HHNeuronInhib_GPe_Adam_Blox <: AbstractInhNeuronBlox ) sts = @variables begin V(t)=-67.00 - n(t)=0.32 + n(t)=0.032 m(t)=0.05 - h(t)=0.59 - mM(t)=0.32 + h(t)=0.059 I_syn(t)=0.0 [input=true] I_in(t)=0.0 @@ -448,6 +453,7 @@ struct HHNeuronInhib_GPe_Adam_Blox <: AbstractInhNeuronBlox I_asc(t)=0.0 [input=true] G(t)=0.0 + [output = true] end ps = @parameters begin @@ -486,9 +492,7 @@ struct HHNeuronInhib_GPe_Adam_Blox <: AbstractInhNeuronBlox D(n)~(αₙ(V)*(1-n)-βₙ(V)*n), D(m)~(αₘ(V)*(1-m)-βₘ(V)*m), D(h)~(αₕ(V)*(1-h)-βₕ(V)*h), - D(mM)~(αₘₘ(V)*(1-mM)-βₘₘ(V)*mM), - D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G) - + D(G)~(-1/τ)*G + G_asymp(V,a,b)*(1-G) ] sys = System( From e7a52174a53b504be11433b0a6e034a218a372e4 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Wed, 22 May 2024 18:09:10 -0400 Subject: [PATCH 10/25] add neuron tests --- test/components.jl | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/test/components.jl b/test/components.jl index 669d1f4f..a3083d08 100644 --- a/test/components.jl +++ b/test/components.jl @@ -357,6 +357,32 @@ end @test sol.retcode == ReturnCode.Success end +@testset "Adam_Brown_HH Neuron_network" begin + nn1 = HHNeuronInhib_MSN_Adam_Blox(name=Symbol("nrn1")) + nn2 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn2"), σ=6) + nn3 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn3"), σ=6) + nn4 = HHNeuronExci_STN_Adam_Blox(name=Symbol("nrn4"), σ=8) + nn5 = HHNeuronInhib_GPe_Adam_Blox(name=Symbol("nrn5"),σ=8) + assembly = [nn1, nn2, nn3, nn4, nn5] + + # Adjacency matrix : + #adj = [0 1 0 + # 0 0 1 + # 0.2 0 0] + g = MetaDiGraph() + add_blox!.(Ref(g), assembly) + add_edge!(g, 1, 2, Dict(:weight=> 0.1)) + add_edge!(g, 2, 3, Dict(:weight=> 0.1, :gap => true, :gap_weight=>0.1)) + add_edge!(g, 3, 4, Dict(:weight=> 0.1)) + add_edge!(g, 4, 5, Dict(:weight=> 0.1)) + + @named neuron_net = system_from_graph(g) + prob = SDEProblem(structural_simplify(neuron_net), [], (0.0, 2), []) + sol = solve(prob, ImplicitEM(),saveat = 0.01,reltol=1e-4,abstol=1e-4) + @test neuron_net isa ODESystem + @test sol.retcode == ReturnCode.Success +end + @testset "NextGenerationEIBlox connected to neuron" begin global_ns = :g @named LC = NextGenerationEIBlox(;namespace=global_ns, Cₑ=2*26,Cᵢ=1*26, Δₑ=0.5, Δᵢ=0.5, η_0ₑ=10.0, η_0ᵢ=0.0, v_synₑₑ=10.0, v_synₑᵢ=-10.0, v_synᵢₑ=10.0, v_synᵢᵢ=-10.0, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑₑ=0.0*26, kₑᵢ=0.6*26, kᵢₑ=0.6*26, kᵢᵢ=0*26) From 0ad6405a2761500ea02e6b572537bcddc79b7c0f Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Thu, 23 May 2024 01:08:12 -0400 Subject: [PATCH 11/25] add composite bloxs --- src/Neuroblox.jl | 3 +- src/blox/DBS_Model_Blox_Adam_Brown.jl | 300 ++++++++++++++++++++++++++ src/blox/connections.jl | 58 ++++- src/blox/cortical_blox.jl | 2 +- 4 files changed, 360 insertions(+), 3 deletions(-) create mode 100644 src/blox/DBS_Model_Blox_Adam_Brown.jl diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 085a8e1d..5a271df4 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -98,6 +98,7 @@ include("blox/neural_mass.jl") include("blox/cortical_blox.jl") include("blox/canonicalmicrocircuit.jl") include("blox/neuron_models.jl") +include("blox/DBS_Model_Blox_Adam_Brown.jl") include("blox/van_der_pol.jl") include("blox/ts_outputs.jl") include("blox/sources.jl") @@ -187,7 +188,7 @@ export JansenRitSPM12, next_generation, qif_neuron, if_neuron, hh_neuron_excitat hh_neuron_inhibitory, van_der_pol export HHNeuronExciBlox, HHNeuronInhibBlox, IFNeuron, LIFNeuron, QIFNeuron, IzhikevichNeuron, CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, - HHNeuronInhib_GPe_Adam_Blox + HHNeuronInhib_GPe_Adam_Blox, Striatum_MSN_Adam, Striatum_FSI_Adam, GPe_Adam, STN_Adam export LinearNeuralMass, HarmonicOscillator, JansenRit, WilsonCowan, LarterBreakspear, NextGenerationBlox, NextGenerationResolvedBlox, NextGenerationEIBlox export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc export HebbianPlasticity, HebbianModulationPlasticity diff --git a/src/blox/DBS_Model_Blox_Adam_Brown.jl b/src/blox/DBS_Model_Blox_Adam_Brown.jl new file mode 100644 index 00000000..2f894ea7 --- /dev/null +++ b/src/blox/DBS_Model_Blox_Adam_Brown.jl @@ -0,0 +1,300 @@ +""" +Subcortical Blox used for DBS model in Adam et al,2021 +""" + +struct Striatum_MSN_Adam <: CompositeBlox + namespace + parts + odesystem + connector + mean + + function Striatum_MSN_Adam(; + name, + namespace = nothing, + N_inhib = 100, + E_syn_inhib=-80, + I_bg=1.19*ones(N_inhib), + freq=zeros(N_inhib), + phase=zeros(N_inhib), + τ_inhib=13, + σ=4.0, + density=0.3, + weight=0.1 + ) + n_inh = [ + HHNeuronInhib_MSN_Adam_Blox( + name = Symbol("inh$i"), + namespace = namespaced_name(namespace, name), + E_syn = E_syn_inhib, + τ = τ_inhib, + I_bg = I_bg[i], + freq = freq[i], + phase = phase[i], + σ=σ + ) + for i in Base.OneTo(N_inhib) + ] + + g = MetaDiGraph() + for i in Base.OneTo(N_inhib) + add_blox!(g, n_inh[i]) + end + in_degree = Int(ceil(density*(N_inhib))) + idxs = Base.OneTo(N_inhib) + for i in idxs + source_set = setdiff(idxs, i) + source = sample(source_set, in_degree; replace=false) + for j in source + add_edge!(g, j, i, Dict(:weight=>weight/in_degree)) + end + end + + parts = n_inh + + bc = connector_from_graph(g) + + sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) + + m = if isnothing(namespace) + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] + else + @variables t + sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] + end + + new(namespace, parts, sys, bc, m) + end + +end + +struct Striatum_FSI_Adam <: CompositeBlox + namespace + parts + odesystem + connector + mean + + function Striatum_FSI_Adam(; + name, + namespace = nothing, + N_inhib = 50, + E_syn_inhib=-80, + I_bg=5.5*ones(N_inhib), + freq=zeros(N_inhib), + phase=zeros(N_inhib), + τ_inhib=11, + τ_inhib_s=6.5, + σ=6.0, + density=0.58, + g_density=0.33, + weight=0.6, + g_weight=0.15 + ) + n_inh = [ + HHNeuronInhib_FSI_Adam_Blox( + name = Symbol("inh$i"), + namespace = namespaced_name(namespace, name), + E_syn = E_syn_inhib, + τ = τ_inhib, + τₛ = τ_inhib_s, + I_bg = I_bg[i], + freq = freq[i], + phase = phase[i], + σ=σ + ) + for i in Base.OneTo(N_inhib) + ] + + g = MetaDiGraph() + for i in Base.OneTo(N_inhib) + add_blox!(g, n_inh[i]) + end + in_degree = Int(ceil(density*(N_inhib))) + gap_degree = Int(ceil(g_density*(N_inhib))) + idxs = Base.OneTo(N_inhib) + + gap_junctions = zeros(Int, N_inhib) + for i in idxs + if gap_junctions[i] x < gap_degree, gap_junctions[other_fsi]) + gap_idx = sample(rem, min(gap_degree, length(rem)); replace=false) + gap_nbr = other_fsi[gap_idx] + gap_junctions[i] += length(gap_idx) + gap_junctions[gap_nbr] .+= 1 + + else + gap_nbr = [] + end + source_set = setdiff(idxs, i) + syn_source = sample(source_set, in_degree; replace=false) + only_syn=setdiff(syn_source,gap_nbr) + only_gap=setdiff(gap_nbr,syn_source) + syn_gap=intersect(syn_source,gap_nbr) + for j in only_syn + add_edge!(g, j, i, Dict(:weight=>weight/in_degree)) + end + for j in only_gap + add_edge!(g, j, i, Dict(:weight=>0, :gap => true, :gap_weight => g_weight/gap_degree)) + end + + for j in syn_gap + add_edge!(g, j, i, Dict(:weight=>weight/in_degree, :gap => true, :gap_weight => g_weight/gap_degree)) + end + + end + + parts = n_inh + + bc = connector_from_graph(g) + + sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) + + m = if isnothing(namespace) + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] + else + @variables t + sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] + end + + new(namespace, parts, sys, bc, m) + end + +end + +struct GPe_Adam <: CompositeBlox + namespace + parts + odesystem + connector + mean + + function GPe_Adam(; + name, + namespace = nothing, + N_inhib = 80, + E_syn_inhib=-80, + I_bg=3*ones(N_inhib), + freq=zeros(N_inhib), + phase=zeros(N_inhib), + τ_inhib=10, + σ=8.0, + density=0.0, + weight=0.0 + ) + n_inh = [ + HHNeuronInhib_MSN_Adam_Blox( + name = Symbol("inh$i"), + namespace = namespaced_name(namespace, name), + E_syn = E_syn_inhib, + τ = τ_inhib, + I_bg = I_bg[i], + freq = freq[i], + phase = phase[i], + σ=σ + ) + for i in Base.OneTo(N_inhib) + ] + + g = MetaDiGraph() + for i in Base.OneTo(N_inhib) + add_blox!(g, n_inh[i]) + end + in_degree = Int(ceil(density*(N_inhib))) + idxs = Base.OneTo(N_inhib) + for i in idxs + source_set = setdiff(idxs, i) + source = sample(source_set, in_degree; replace=false) + for j in source + add_edge!(g, j, i, Dict(:weight=>weight/in_degree)) + end + end + + parts = n_inh + + bc = connector_from_graph(g) + + sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) + + m = if isnothing(namespace) + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] + else + @variables t + sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] + end + + new(namespace, parts, sys, bc, m) + end + +end + +struct STN_Adam <: CompositeBlox + namespace + parts + odesystem + connector + mean + + function STN_Adam(; + name, + namespace = nothing, + N_exci = 40, + E_syn_exci=0.0, + I_bg=1.9*ones(N_exci), + freq=zeros(N_exci), + phase=zeros(N_exci), + τ_exci=2, + σ=8.0, + density=0.0, + weight=0.0 + ) + n_exci = [ + HHNeuronExci_STN_Adam_Blox( + name = Symbol("exci$i"), + namespace = namespaced_name(namespace, name), + E_syn = E_syn_exci, + τ = τ_exci, + I_bg = I_bg[i], + freq = freq[i], + phase = phase[i], + σ=σ + ) + for i in Base.OneTo(N_exci) + ] + + g = MetaDiGraph() + for i in Base.OneTo(N_exci) + add_blox!(g, n_exci[i]) + end + in_degree = Int(ceil(density*(N_exci))) + idxs = Base.OneTo(N_exci) + for i in idxs + source_set = setdiff(idxs, i) + source = sample(source_set, in_degree; replace=false) + for j in source + add_edge!(g, j, i, Dict(:weight=>weight/in_degree)) + end + end + + parts = n_exci + + bc = connector_from_graph(g) + + sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(parts; name) + + m = if isnothing(namespace) + [s for s in unknowns.((sys,), unknowns(sys)) if contains(string(s), "V(t)")] + else + @variables t + sys_namespace = System(Equation[], t; name=namespaced_name(namespace, name)) + [s for s in unknowns.((sys_namespace,), unknowns(sys)) if contains(string(s), "V(t)")] + end + + new(namespace, parts, sys, bc, m) + end + +end \ No newline at end of file diff --git a/src/blox/connections.jl b/src/blox/connections.jl index aaf529c4..833b1255 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -136,7 +136,7 @@ function params(bc::BloxConnector) end function (bc::BloxConnector)( - HH_out::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}, + HH_out::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}, HH_in::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronInhib_FSI_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}; kwargs... ) @@ -154,12 +154,68 @@ function (bc::BloxConnector)( end STA = get_sta(kwargs, nameof(HH_out), nameof(HH_in)) + + eq = if STA sys_in.I_syn ~ -w * sys_in.Gₛₜₚ * sys_out.G * (sys_in.V - sys_out.E_syn) else sys_in.I_syn ~ -w * sys_out.G * (sys_in.V - sys_out.E_syn) end + accumulate_equation!(bc, eq) + + GAP = get_gap(kwargs, nameof(HH_out), nameof(HH_in)) + if GAP + w_gap = generate_gap_weight_param(HH_out, HH_in; kwargs...) + push!(bc.weights, w_gap) + eq2 = sys_in.I_gap ~ -w_gap * (sys_in.V - sys_out.V) + accumulate_equation!(bc, eq2) + eq3 = sys_out.I_gap ~ -w_gap * (sys_out.V - sys_in.V) + accumulate_equation!(bc, eq3) + end + +end + +function (bc::BloxConnector)( + HH_out::HHNeuronInhib_FSI_Adam_Blox, + HH_in::Union{HHNeuronExciBlox, HHNeuronInhibBlox, HHNeuronInhib_MSN_Adam_Blox, HHNeuronExci_STN_Adam_Blox, HHNeuronInhib_GPe_Adam_Blox}; + kwargs... +) + sys_out = get_namespaced_sys(HH_out) + sys_in = get_namespaced_sys(HH_in) + + w = generate_weight_param(HH_out, HH_in; kwargs...) + push!(bc.weights, w) + + eq = sys_in.I_syn ~ -w * sys_out.G * (sys_in.V - sys_out.E_syn) + + accumulate_equation!(bc, eq) + + GAP = get_gap(kwargs, nameof(HH_out), nameof(HH_in)) + if GAP + w_gap = generate_gap_weight_param(HH_out, HH_in; kwargs...) + push!(bc.weights, w_gap) + eq2 = sys_in.I_gap ~ -w_gap * (sys_in.V - sys_out.V) + accumulate_equation!(bc, eq2) + eq3 = sys_out.I_gap ~ -w_gap * (sys_out.V - sys_in.V) + accumulate_equation!(bc, eq3) + end +end + +function (bc::BloxConnector)( + HH_out::HHNeuronInhib_FSI_Adam_Blox, + HH_in::HHNeuronInhib_FSI_Adam_Blox; + kwargs... +) + sys_out = get_namespaced_sys(HH_out) + sys_in = get_namespaced_sys(HH_in) + + w = generate_weight_param(HH_out, HH_in; kwargs...) + push!(bc.weights, w) + + + eq = sys_in.I_syn ~ -w * sys_out.Gₛ * (sys_in.V - sys_out.E_syn) + accumulate_equation!(bc, eq) GAP = get_gap(kwargs, nameof(HH_out), nameof(HH_in)) diff --git a/src/blox/cortical_blox.jl b/src/blox/cortical_blox.jl index 16793ae9..a59253c7 100644 --- a/src/blox/cortical_blox.jl +++ b/src/blox/cortical_blox.jl @@ -6,7 +6,7 @@ struct CorticalBlox <: CompositeBlox function CorticalBlox(; name, - N_wta, + N_wta=20, namespace=nothing, N_exci=5, E_syn_exci=0.0, From 479497d43f7d770faf0eae764825f0f7661cb952 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Thu, 23 May 2024 14:17:00 -0400 Subject: [PATCH 12/25] complete DBS circuit and tests --- examples/Adab_DBS_example.jl | 26 + examples/Adab_DBS_example_notebook.jl | 3449 +++++++++++++++++++++++++ src/blox/connections.jl | 44 + test/DBS_Model_Adam_Brown_test.jl | 59 + test/components.jl | 26 - 5 files changed, 3578 insertions(+), 26 deletions(-) create mode 100644 examples/Adab_DBS_example.jl create mode 100644 examples/Adab_DBS_example_notebook.jl create mode 100644 test/DBS_Model_Adam_Brown_test.jl diff --git a/examples/Adab_DBS_example.jl b/examples/Adab_DBS_example.jl new file mode 100644 index 00000000..784856b2 --- /dev/null +++ b/examples/Adab_DBS_example.jl @@ -0,0 +1,26 @@ +using Neuroblox +using DifferentialEquations +using Graphs +using MetaGraphs +using Plots + +global_ns = :g +@named msn = Striatum_MSN_Adam(namespace=global_ns) +@named fsi = Striatum_FSI_Adam(namespace=global_ns) +@named gpe = GPe_Adam(namespace=global_ns) +@named stn = STN_Adam(namespace=global_ns) + +assembly = [msn, fsi, gpe, stn] +g = MetaDiGraph() +add_blox!.(Ref(g), assembly) + +add_edge!(g, 1, 3, Dict(:weight=> 2.5/100, :density=>0.33)) +add_edge!(g, 2, 1, Dict(:weight=> 0.6/50, :density=>0.15)) +add_edge!(g, 3, 4, Dict(:weight=> 0.3/80, :density=>0.05)) +add_edge!(g, 4, 2, Dict(:weight=> 0.165/40, :density=>0.1)) + +@named neuron_net = system_from_graph(g) +sys = structural_simplify(neuron_net) +prob = SDEProblem(sys, [], (0.0, 500), []) +sol = solve(prob, saveat = 0.01) +ss=convert(Array,sol) \ No newline at end of file diff --git a/examples/Adab_DBS_example_notebook.jl b/examples/Adab_DBS_example_notebook.jl new file mode 100644 index 00000000..e2df04d9 --- /dev/null +++ b/examples/Adab_DBS_example_notebook.jl @@ -0,0 +1,3449 @@ +### A Pluto.jl notebook ### +# v0.19.42 + +using Markdown +using InteractiveUtils + +# ╔═╡ 1d4c6b44-1928-11ef-2985-ff4ed2472f90 +using Neuroblox + +# ╔═╡ ffedfb4d-44fe-43ca-9c5f-0ae41b724c20 +using DifferentialEquations + +# ╔═╡ 74a59599-e42b-4a19-8b59-529e8b3d8bb8 +using Graphs + +# ╔═╡ 0c2b0150-5d2b-48a1-9487-634bde3ff563 +using MetaGraphs + +# ╔═╡ 607a2e26-2095-43ae-a221-f3d5e95f8401 +using Plots + +# ╔═╡ 9ca48018-bc7f-4110-b3aa-800b7dea409f +begin +global_ns = :g +@named msn = Striatum_MSN_Adam(namespace=global_ns) +@named fsi = Striatum_FSI_Adam(namespace=global_ns) +@named gpe = GPe_Adam(namespace=global_ns) +@named stn = STN_Adam(namespace=global_ns) + +assembly = [msn, fsi, gpe, stn] +g = MetaDiGraph() +add_blox!.(Ref(g), assembly) + +add_edge!(g, 1, 3, Dict(:weight=> 2.5/100, :density=>0.33)) +add_edge!(g, 2, 1, Dict(:weight=> 0.6/50, :density=>0.15)) +add_edge!(g, 3, 4, Dict(:weight=> 0.3/80, :density=>0.05)) +add_edge!(g, 4, 2, Dict(:weight=> 0.165/40, :density=>0.1)) +end + +# ╔═╡ 2a27de07-7928-439a-9bc8-bffc1fd760c4 +@named neuron_net = system_from_graph(g) + +# ╔═╡ 89568f43-b260-4a98-8128-2d2313115d47 +sys = structural_simplify(neuron_net) + +# ╔═╡ d56cd79b-5ced-4aa3-bbb9-5a337e40bb8b +prob = SDEProblem(sys, [], (0.0, 500), []) + +# ╔═╡ d25da273-b090-4ec8-bc58-f133eef00b68 +sol = solve(prob, saveat = 0.01) + +# ╔═╡ 10f22f39-acda-4ca3-aa4a-7dc8e10c2884 +ss=convert(Array,sol) + +# ╔═╡ 43cdb545-4bc0-4b79-a671-3f9bb53c6b75 + + +# ╔═╡ 00000000-0000-0000-0000-000000000001 +PLUTO_PROJECT_TOML_CONTENTS = """ +[deps] +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" +MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5" +Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" + +[compat] +DifferentialEquations = "~7.11.0" +Graphs = "~1.11.0" +MetaGraphs = "~0.7.2" +Neuroblox = "~0.3.0" +Plots = "~1.40.4" +""" + +# ╔═╡ 00000000-0000-0000-0000-000000000002 +PLUTO_MANIFEST_TOML_CONTENTS = """ +# This file is machine-generated - editing it directly is not advised + +julia_version = "1.10.3" +manifest_format = "2.0" +project_hash = "c7fbbb971bd47406db4ed7b3feb2b27f9a07b542" + +[[deps.ADTypes]] +git-tree-sha1 = "016833eb52ba2d6bea9fcb50ca295980e728ee24" +uuid = "47edcb42-4c32-4615-8424-f2b9edc5f35b" +version = "0.2.7" + +[[deps.ARFFFiles]] +deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"] +git-tree-sha1 = "e8c8e0a2be6eb4f56b1672e46004463033daa409" +uuid = 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"dfaa095f-4041-5dcd-9319-2fabd8486b76" +version = "3.5.0+0" + +[[deps.xkbcommon_jll]] +deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"] +git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37" +uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd" +version = "1.4.1+1" +""" + +# ╔═╡ Cell order: +# ╠═1d4c6b44-1928-11ef-2985-ff4ed2472f90 +# ╠═ffedfb4d-44fe-43ca-9c5f-0ae41b724c20 +# ╠═74a59599-e42b-4a19-8b59-529e8b3d8bb8 +# ╠═0c2b0150-5d2b-48a1-9487-634bde3ff563 +# ╠═607a2e26-2095-43ae-a221-f3d5e95f8401 +# ╠═9ca48018-bc7f-4110-b3aa-800b7dea409f +# ╠═2a27de07-7928-439a-9bc8-bffc1fd760c4 +# ╠═89568f43-b260-4a98-8128-2d2313115d47 +# ╠═d56cd79b-5ced-4aa3-bbb9-5a337e40bb8b +# ╠═d25da273-b090-4ec8-bc58-f133eef00b68 +# ╠═10f22f39-acda-4ca3-aa4a-7dc8e10c2884 +# ╠═43cdb545-4bc0-4b79-a671-3f9bb53c6b75 +# ╟─00000000-0000-0000-0000-000000000001 +# ╟─00000000-0000-0000-0000-000000000002 diff --git a/src/blox/connections.jl b/src/blox/connections.jl index 833b1255..27eaf62c 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -120,6 +120,17 @@ function hypergeometric_connections!(bc, neurons_out, neurons_in, name_out, name end end +function indegree_constrained_connections!(bc, neurons_out, neurons_in, name_out, name_in; kwargs...) + density = get_density(kwargs, name_out, name_in) + in_degree = Int(ceil(density * length(neurons_out))) + for neuron_postsyn in neurons_in + idx = sample(collect(1:length(neurons_out)), in_degree; replace=false) + for neuron_presyn in neurons_out[idx] + bc(neuron_presyn, neuron_postsyn; kwargs...) + end + end +end + """ Helper to merge delays and weights into a single vector """ @@ -229,6 +240,39 @@ function (bc::BloxConnector)( end end +function (bc::BloxConnector)( + cb_out::Union{Striatum_MSN_Adam,Striatum_FSI_Adam,GPe_Adam}, + cb_in::Union{Striatum_MSN_Adam,Striatum_FSI_Adam,GPe_Adam}; + kwargs... +) + neurons_in = get_inh_neurons(cb_in) + neurons_out = get_inh_neurons(cb_out) + + indegree_constrained_connections!(bc, neurons_out, neurons_in, nameof(cb_out), nameof(cb_in); kwargs...) +end + +function (bc::BloxConnector)( + cb_out::STN_Adam, + cb_in::Union{Striatum_MSN_Adam,Striatum_FSI_Adam,GPe_Adam}; + kwargs... +) + neurons_in = get_inh_neurons(cb_in) + neurons_out = get_exci_neurons(cb_out) + + indegree_constrained_connections!(bc, neurons_out, neurons_in, nameof(cb_out), nameof(cb_in); kwargs...) +end + +function (bc::BloxConnector)( + cb_out::Union{Striatum_MSN_Adam,Striatum_FSI_Adam,GPe_Adam}, + cb_in::STN_Adam; + kwargs... +) + neurons_in = get_exci_neurons(cb_in) + neurons_out = get_inh_neurons(cb_out) + + indegree_constrained_connections!(bc, neurons_out, neurons_in, nameof(cb_out), nameof(cb_in); kwargs...) +end + function (bc::BloxConnector)( asc_out::NextGenerationEIBlox, HH_in::Union{HHNeuronExciBlox, HHNeuronInhibBlox}; diff --git a/test/DBS_Model_Adam_Brown_test.jl b/test/DBS_Model_Adam_Brown_test.jl new file mode 100644 index 00000000..ead6b9a3 --- /dev/null +++ b/test/DBS_Model_Adam_Brown_test.jl @@ -0,0 +1,59 @@ +using Neuroblox +using DifferentialEquations +using Test +using Graphs +using MetaGraphs + +#test for components used in Adam model for DBS + +@testset "Adam_Brown_HH Neuron_network" begin + nn1 = HHNeuronInhib_MSN_Adam_Blox(name=Symbol("nrn1")) + nn2 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn2"), σ=6) + nn3 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn3"), σ=6) + nn4 = HHNeuronExci_STN_Adam_Blox(name=Symbol("nrn4"), σ=8) + nn5 = HHNeuronInhib_GPe_Adam_Blox(name=Symbol("nrn5"),σ=8) + assembly = [nn1, nn2, nn3, nn4, nn5] + + # Adjacency matrix : + #adj = [0 1 0 + # 0 0 1 + # 0.2 0 0] + g = MetaDiGraph() + add_blox!.(Ref(g), assembly) + add_edge!(g, 1, 2, Dict(:weight=> 0.1)) + add_edge!(g, 2, 3, Dict(:weight=> 0.1, :gap => true, :gap_weight=>0.1)) + add_edge!(g, 3, 4, Dict(:weight=> 0.1)) + add_edge!(g, 4, 5, Dict(:weight=> 0.1)) + + @named neuron_net = system_from_graph(g) + prob = SDEProblem(structural_simplify(neuron_net), [], (0.0, 2), []) + sol = solve(prob, ImplicitEM(),saveat = 0.01,reltol=1e-4,abstol=1e-4) + @test neuron_net isa ODESystem + @test sol.retcode == ReturnCode.Success +end + + + +@testset "DBS circuit" begin + global_ns = :g + @named msn = Striatum_MSN_Adam(namespace=global_ns, N_inhib=2) + @named fsi = Striatum_FSI_Adam(namespace=global_ns,N_inhib=5) + @named gpe = GPe_Adam(namespace=global_ns,N_inhib=2) + @named stn = STN_Adam(namespace=global_ns,N_exci=2) + + assembly = [msn, fsi, gpe, stn] + g = MetaDiGraph() + add_blox!.(Ref(g), assembly) + + add_edge!(g, 1, 3, Dict(:weight=> 2.5/100, :density=>0.5)) + add_edge!(g, 2, 1, Dict(:weight=> 0.6/50, :density=>0.5)) + add_edge!(g, 3, 4, Dict(:weight=> 0.3/80, :density=>0.5)) + add_edge!(g, 4, 2, Dict(:weight=> 0.165/40, :density=>0.5)) + + @named neuron_net = system_from_graph(g) + sys = structural_simplify(neuron_net) + prob = SDEProblem(sys, [], (0.0, 2), []) + sol = solve(prob) + @testsys isa ODESystem + @test sol.retcode == ReturnCode.Success +end \ No newline at end of file diff --git a/test/components.jl b/test/components.jl index a3083d08..669d1f4f 100644 --- a/test/components.jl +++ b/test/components.jl @@ -357,32 +357,6 @@ end @test sol.retcode == ReturnCode.Success end -@testset "Adam_Brown_HH Neuron_network" begin - nn1 = HHNeuronInhib_MSN_Adam_Blox(name=Symbol("nrn1")) - nn2 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn2"), σ=6) - nn3 = HHNeuronInhib_FSI_Adam_Blox(name=Symbol("nrn3"), σ=6) - nn4 = HHNeuronExci_STN_Adam_Blox(name=Symbol("nrn4"), σ=8) - nn5 = HHNeuronInhib_GPe_Adam_Blox(name=Symbol("nrn5"),σ=8) - assembly = [nn1, nn2, nn3, nn4, nn5] - - # Adjacency matrix : - #adj = [0 1 0 - # 0 0 1 - # 0.2 0 0] - g = MetaDiGraph() - add_blox!.(Ref(g), assembly) - add_edge!(g, 1, 2, Dict(:weight=> 0.1)) - add_edge!(g, 2, 3, Dict(:weight=> 0.1, :gap => true, :gap_weight=>0.1)) - add_edge!(g, 3, 4, Dict(:weight=> 0.1)) - add_edge!(g, 4, 5, Dict(:weight=> 0.1)) - - @named neuron_net = system_from_graph(g) - prob = SDEProblem(structural_simplify(neuron_net), [], (0.0, 2), []) - sol = solve(prob, ImplicitEM(),saveat = 0.01,reltol=1e-4,abstol=1e-4) - @test neuron_net isa ODESystem - @test sol.retcode == ReturnCode.Success -end - @testset "NextGenerationEIBlox connected to neuron" begin global_ns = :g @named LC = NextGenerationEIBlox(;namespace=global_ns, Cₑ=2*26,Cᵢ=1*26, Δₑ=0.5, Δᵢ=0.5, η_0ₑ=10.0, η_0ᵢ=0.0, v_synₑₑ=10.0, v_synₑᵢ=-10.0, v_synᵢₑ=10.0, v_synᵢᵢ=-10.0, alpha_invₑₑ=10.0/26, alpha_invₑᵢ=0.8/26, alpha_invᵢₑ=10.0/26, alpha_invᵢᵢ=0.8/26, kₑₑ=0.0*26, kₑᵢ=0.6*26, kᵢₑ=0.6*26, kᵢᵢ=0*26) From c67e6dee40fac575dcafe68755f1199cad3833ea Mon Sep 17 00:00:00 2001 From: Mason Protter Date: Thu, 23 May 2024 23:49:34 +0200 Subject: [PATCH 13/25] Patch for test failues (#353) * avoid parameter indexing error * use explicitly declared solver * bump version --- Project.toml | 2 +- src/blox/reinforcement_learning.jl | 6 +++--- test/components.jl | 12 ++++++------ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/Project.toml b/Project.toml index 29cc312a..f9511e09 100644 --- a/Project.toml +++ b/Project.toml @@ -1,7 +1,7 @@ name = "Neuroblox" uuid = "769b91e5-4c60-41ee-bfae-153c84203cb2" repo = "https://github.com/Neuroblox/Neuroblox.jl.git" -version = "0.4.0" +version = "0.4.1" [deps] AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c" diff --git a/src/blox/reinforcement_learning.jl b/src/blox/reinforcement_learning.jl index 05a44dd3..7d2044cf 100644 --- a/src/blox/reinforcement_learning.jl +++ b/src/blox/reinforcement_learning.jl @@ -67,9 +67,9 @@ end function weight_gradient(hmp::HebbianModulationPlasticity, sol, w, feedback) state_mod = get_modulator_state(hmp.modulator) - val_pre = only(sol(hmp.t_pre; idxs = [hmp.state_pre])) - val_post = only(sol(hmp.t_post; idxs = [hmp.state_post])) - val_mod = only(sol(hmp.t_mod; idxs = [state_mod])) + val_pre = sol(hmp.t_pre; idxs = hmp.state_pre) + val_post = sol(hmp.t_post; idxs = hmp.state_post) + val_mod = sol(hmp.t_mod; idxs = state_mod) return hmp(val_pre, val_post, val_mod, w, feedback) end diff --git a/test/components.jl b/test/components.jl index 669d1f4f..bbbe33fd 100644 --- a/test/components.jl +++ b/test/components.jl @@ -510,7 +510,7 @@ end sys = system_from_graph(g; name=namespace=global_ns) sys_simpl =structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0,2)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success  end @@ -524,7 +524,7 @@ end @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 100.0)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success end @@ -538,7 +538,7 @@ end @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 200.0)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success  end @@ -552,7 +552,7 @@ end @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 200.0)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success end @@ -566,7 +566,7 @@ end @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 200.0)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success end @@ -577,6 +577,6 @@ end @named sys = system_from_graph(g) sys_simpl = structural_simplify(sys) prob = ODEProblem(sys_simpl, [], (0, 200.0)) - sol = solve(prob) + sol = solve(prob, Tsit5()) @test sol.retcode == ReturnCode.Success end From 96fabec25a66ddb228af1a97dd783d0522236a6d Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Mon, 3 Jun 2024 17:02:47 -0400 Subject: [PATCH 14/25] added Generic2dOsc with example script - not working --- examples/ordered_labels.csv | 10 +++++ examples/tract_lengths.csv | 76 +++++++++++++++++++++++++++++++++ examples/weights.csv | 77 ++++++++++++++++++++++++++++++++++ examples/whole_brain_2dosc.jl | 23 ++++++++++ src/blox/neural_mass.jl | 79 +++++++++++++++++++++++++++++++++++ 5 files changed, 265 insertions(+) create mode 100644 examples/ordered_labels.csv create mode 100644 examples/tract_lengths.csv create mode 100644 examples/weights.csv create mode 100644 examples/whole_brain_2dosc.jl diff --git a/examples/ordered_labels.csv b/examples/ordered_labels.csv new file mode 100644 index 00000000..a8acb7a7 --- /dev/null +++ b/examples/ordered_labels.csv @@ -0,0 +1,10 @@ +lA1, lA2, lAMYG, lCCA, lCCP, lCCR, lCCS, lFEF, +lG, lHC, lIA, lIP, lM1, lPCI, lPCIP, lPCM, lPCS, +lPFCCL, lPFCDL, lPFCDM, lPFCM, lPFCORB, lPFCPOL, +lPFCVL, lPHC, lPMCDL, lPMCM, lPMCVL, lS1, lS2, +lTCC, lTCI, lTCPOL, lTCS, lTCV, lV1, lV2, lCC, +rA1, rA2, rAMYG, rCCA, rCCP, rCCR, rCCS, rFEF, +rG, rHC, rIA, rIP, rM1, rPCI, rPCIP, rPCM, rPCS, +rPFCCL, rPFCDL, rPFCDM, rPFCM, rPFCORB, rPFCPOL, +rPFCVL, rPHC, rPMCDL, rPMCM, rPMCVL, rS1, rS2, +rTCC, rTCI, rTCPOL, rTCS, rTCV, rV1, rV2, rCC \ No newline at end of file diff --git a/examples/tract_lengths.csv b/examples/tract_lengths.csv new file mode 100644 index 00000000..2e604325 --- /dev/null +++ b/examples/tract_lengths.csv @@ -0,0 +1,76 @@ 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diff --git a/examples/weights.csv b/examples/weights.csv new file mode 100644 index 00000000..f9decf2e --- /dev/null +++ b/examples/weights.csv @@ -0,0 +1,77 @@ +lA1, lA2, lAMYG, lCCA, lCCP, lCCR, lCCS, lFEF, lG, lHC, lIA, lIP, lM1, lPCI, lPCIP, lPCM, lPCS, lPFCCL, lPFCDL, lPFCDM, lPFCM, lPFCORB, lPFCPOL, lPFCVL, lPHC, lPMCDL, lPMCM, lPMCVL, lS1, lS2, lTCC, lTCI, lTCPOL, lTCS, lTCV, lV1, lV2, lCC, rA1, rA2, rAMYG, rCCA, rCCP, rCCR, rCCS, rFEF, rG, rHC, rIA, rIP, rM1, rPCI, rPCIP, rPCM, rPCS, rPFCCL, rPFCDL, rPFCDM, rPFCM, rPFCORB, rPFCPOL, rPFCVL, rPHC, rPMCDL, rPMCM, rPMCVL, rS1, rS2, rTCC, rTCI, rTCPOL, rTCS, rTCV, rV1, rV2, rCC 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diff --git a/examples/whole_brain_2dosc.jl b/examples/whole_brain_2dosc.jl new file mode 100644 index 00000000..36cfd7c8 --- /dev/null +++ b/examples/whole_brain_2dosc.jl @@ -0,0 +1,23 @@ +# this is an example that reproduces the Virtual Brain tutorial on Resting State networks +using Neuroblox +using CSV +using DataFrames +using MetaGraphs +using DifferentialEquations + +weights = CSV.read("examples/weights.csv",DataFrame) +region_names = names(weights) + +wm = Array(weights) + +blocks = [] +for i in 1:size(wm)[1] + push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]))) +end + +g = MetaDiGraph() +add_blox!.(Ref(g), blocks) +create_adjacency_edges!(g, wm) + +@named sys = system_from_graph(g) +sys = structural_simplify(sys) diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index d00294ab..e14dcec9 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -355,3 +355,82 @@ struct LarterBreakspear <: NeuralMassBlox new(p, sts[5], sts[4], sys, namespace) end end + +""" + Generic2dOscillator(name, namespace, ...) + + The Generic2dOscillator model is a generic dynamic system with two state + variables. The dynamic equations of this model are composed of two ordinary + differential equations comprising two nullclines. The first nullcline is a + cubic function as it is found in most neuron and population models; the + second nullcline is arbitrarily configurable as a polynomial function up to + second order. The manipulation of the latter nullcline's parameters allows + to generate a wide range of different behaviours. + + Equations: + + ```math + \\begin{align} + \\dot{V} &= d \\, \\tau (-f V^3 + e V^2 + g V + \\alpha W + \\gamma I) \\\\ + \\dot{W} &= \\dfrac{d}{\tau}\\,\\,(c V^2 + b V - \\beta W + a) + \\end{align} + ``` + +Arguments: +- name: Name given to ODESystem object within the blox. +- namespace: Additional namespace above name if needed for inheritance. +- Other parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds. + +Citations: +FitzHugh, R., Impulses and physiological states in theoretical +models of nerve membrane, Biophysical Journal 1: 445, 1961. + +Nagumo et.al, An Active Pulse Transmission Line Simulating +Nerve Axon, Proceedings of the IRE 50: 2061, 1962. + +Stefanescu, R., Jirsa, V.K. Reduced representations of +heterogeneous mixed neural networks with synaptic coupling. +Physical Review E, 83, 2011. + +Jirsa VK, Stefanescu R. Neural population modes capture +biologically realistic large-scale network dynamics. Bulletin of +Mathematical Biology, 2010. + +Stefanescu, R., Jirsa, V.K. A low dimensional description +of globally coupled heterogeneous neural networks of excitatory and +inhibitory neurons. PLoS Computational Biology, 4(11), 2008). + +""" +struct Generic2dOscillator <: NeuralMassBlox + params + output + jcn + odesystem + namespace + function Generic2dOscillator(; + name, + namespace=nothing, + τ=-1.0, + a=-2.0, + b=-10.0, + c=0.0, + d=0.02, + e=3.0, + f=1.0, + g=0.0, + α=1.0, + β=1.0, + γ=6e-2, + bn=5e-4, + ) + p = paramscoping(τ=τ, a=a,b=b,c=c,d=d,e=e,f=f,g=g,α=α,β=β,γ=γ) + τ,a,b,c,d,e,f,g,α,β,γ = p + + sts = @variables V(t)=0.0 [output = true] W(t)=1.0 jcn(t)=0.0 [input=true] + @brownian w + eqs = [ D(V) ~ d * τ * ( -f * V^3 + e * V^2 + g * V + α * W + γ * jcn) + bn * w, + D(W) ~ d / τ * ( c * V^2 + b * V - β * W + a) + bn * w] + sys = System(eqs, t; name=name) + new(p, sts[1], sts[3], sys, namespace) + end +end From 38948912edf0d585650fd3db20eb0d4fe61ece4c Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Mon, 3 Jun 2024 19:41:46 -0400 Subject: [PATCH 15/25] stochastic simulation is now working --- examples/whole_brain_2dosc.jl | 12 ++++++++++++ src/blox/blox_utilities.jl | 2 +- src/blox/neural_mass.jl | 4 ++-- 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/examples/whole_brain_2dosc.jl b/examples/whole_brain_2dosc.jl index 36cfd7c8..85ea22eb 100644 --- a/examples/whole_brain_2dosc.jl +++ b/examples/whole_brain_2dosc.jl @@ -4,6 +4,9 @@ using CSV using DataFrames using MetaGraphs using DifferentialEquations +using Random +using Plots +using Statistics weights = CSV.read("examples/weights.csv",DataFrame) region_names = names(weights) @@ -21,3 +24,12 @@ create_adjacency_edges!(g, wm) @named sys = system_from_graph(g) sys = structural_simplify(sys) + +prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 10*60e3), []) +@time sol = solve(prob, EulerHeun(), dt=0.5, saveat=5) + +plot(sol.t,sol[5,:],xlims=(0,10000)) + +solv = Array(sol[1:2:end,1000:2000])' +cor_matrix = cor(solv) +heatmap(atanh.(cor_matrix)) diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl index 8de8b461..81a89638 100644 --- a/src/blox/blox_utilities.jl +++ b/src/blox/blox_utilities.jl @@ -56,7 +56,7 @@ get_sys(sys::AbstractODESystem) = sys function get_namespaced_sys(blox) sys = get_sys(blox) - ODESystem( + System( equations(sys), only(independent_variables(sys)), unknowns(sys), diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index e14dcec9..c2223986 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -410,7 +410,7 @@ struct Generic2dOscillator <: NeuralMassBlox function Generic2dOscillator(; name, namespace=nothing, - τ=-1.0, + τ=1.0, a=-2.0, b=-10.0, c=0.0, @@ -430,7 +430,7 @@ struct Generic2dOscillator <: NeuralMassBlox @brownian w eqs = [ D(V) ~ d * τ * ( -f * V^3 + e * V^2 + g * V + α * W + γ * jcn) + bn * w, D(W) ~ d / τ * ( c * V^2 + b * V - β * W + a) + bn * w] - sys = System(eqs, t; name=name) + sys = System(eqs, t, sts, p; name=name) new(p, sts[1], sts[3], sys, namespace) end end From 47fa5ef105e7263917091000662b114a9f6df921 Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Mon, 3 Jun 2024 23:38:48 -0400 Subject: [PATCH 16/25] fully functioning example --- examples/whole_brain_2dosc.jl | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/examples/whole_brain_2dosc.jl b/examples/whole_brain_2dosc.jl index 85ea22eb..1d4b1af3 100644 --- a/examples/whole_brain_2dosc.jl +++ b/examples/whole_brain_2dosc.jl @@ -7,6 +7,7 @@ using DifferentialEquations using Random using Plots using Statistics +using HypothesisTests weights = CSV.read("examples/weights.csv",DataFrame) region_names = names(weights) @@ -15,7 +16,7 @@ wm = Array(weights) blocks = [] for i in 1:size(wm)[1] - push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]))) + push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]),bn=sqrt(5e-4))) end g = MetaDiGraph() @@ -30,6 +31,23 @@ prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 10*60e3), []) plot(sol.t,sol[5,:],xlims=(0,10000)) -solv = Array(sol[1:2:end,1000:2000])' -cor_matrix = cor(solv) -heatmap(atanh.(cor_matrix)) +solv = Array(sol[1:2:end,1:1000])' +cs = [] +for i in 1:Int((length(sol.t)-1)/1000)-1 + solv = Array(sol[1:2:end,(i-1)*1000+1:(i*1000)])' + push!(cs,cor(solv)) +end +css = stack(cs) +cssath = atanh.(css) +for i in 1:size(cssath)[1] + cssath[i,i,:] = zeros(size(cssath)[3]) +end +p = zeros(76,76) +for i in 1:76 + for j in 1:76 + p[i,j] = pvalue(OneSampleTTest(css[i,j,:])) + end +end +p +heatmap(-log10.(p) .* (p .< 0.05)) +heatmap(wm) From 695911d0599791a370baa25d6f9e277943967a24 Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Tue, 4 Jun 2024 00:53:37 -0400 Subject: [PATCH 17/25] got sign of coupling wrong - now it reproduces the tutorial --- examples/whole_brain_2dosc.jl | 8 ++++---- src/blox/neural_mass.jl | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/whole_brain_2dosc.jl b/examples/whole_brain_2dosc.jl index 1d4b1af3..ae14095d 100644 --- a/examples/whole_brain_2dosc.jl +++ b/examples/whole_brain_2dosc.jl @@ -27,11 +27,11 @@ create_adjacency_edges!(g, wm) sys = structural_simplify(sys) prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 10*60e3), []) -@time sol = solve(prob, EulerHeun(), dt=0.5, saveat=5) - +@time sol = solve(prob, EulerHeun(), dt=0.5) +df = DataFrame(sol) +names(df) plot(sol.t,sol[5,:],xlims=(0,10000)) -solv = Array(sol[1:2:end,1:1000])' cs = [] for i in 1:Int((length(sol.t)-1)/1000)-1 solv = Array(sol[1:2:end,(i-1)*1000+1:(i*1000)])' @@ -49,5 +49,5 @@ for i in 1:76 end end p -heatmap(-log10.(p) .* (p .< 0.05)) +heatmap(log10.(p) .* (p .< 0.05)) heatmap(wm) diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index c2223986..b8621854 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -428,7 +428,7 @@ struct Generic2dOscillator <: NeuralMassBlox sts = @variables V(t)=0.0 [output = true] W(t)=1.0 jcn(t)=0.0 [input=true] @brownian w - eqs = [ D(V) ~ d * τ * ( -f * V^3 + e * V^2 + g * V + α * W + γ * jcn) + bn * w, + eqs = [ D(V) ~ d * τ * ( -f * V^3 + e * V^2 + g * V + α * W - γ * jcn) + bn * w, D(W) ~ d / τ * ( c * V^2 + b * V - β * W + a) + bn * w] sys = System(eqs, t, sts, p; name=name) new(p, sts[1], sts[3], sys, namespace) From 4d41fe5bfe80dd04bbc4c0ab7901ea52a9de90a4 Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Tue, 4 Jun 2024 14:40:49 -0400 Subject: [PATCH 18/25] added resting state whole brain simulation: --- docs/Project.toml | 7 ++- docs/build/assets/Project.toml | 15 +++++ docs/build/assets/favicon.ico | Bin 0 -> 15406 bytes docs/build/assets/logo.png | Bin 0 -> 1543420 bytes docs/build/data/tract_lengths.csv | 76 ++++++++++++++++++++++++ docs/build/data/weights.csv | 77 ++++++++++++++++++++++++ docs/pages.jl | 4 +- docs/src/assets/Project.toml | 15 +++++ docs/src/data/tract_lengths.csv | 76 ++++++++++++++++++++++++ docs/src/data/weights.csv | 77 ++++++++++++++++++++++++ docs/src/tutorials/resting_state_wb.md | 78 +++++++++++++++++++++++++ 11 files changed, 423 insertions(+), 2 deletions(-) create mode 100644 docs/build/assets/Project.toml create mode 100644 docs/build/assets/favicon.ico create mode 100644 docs/build/assets/logo.png create mode 100644 docs/build/data/tract_lengths.csv create mode 100644 docs/build/data/weights.csv create mode 100644 docs/src/assets/Project.toml create mode 100644 docs/src/data/tract_lengths.csv create mode 100644 docs/src/data/weights.csv create mode 100644 docs/src/tutorials/resting_state_wb.md diff --git a/docs/Project.toml b/docs/Project.toml index 9ef60e35..f7d5dbc0 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,10 +1,15 @@ [deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" -Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2" +HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5" +Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2" Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [compat] Documenter = "1" diff --git a/docs/build/assets/Project.toml b/docs/build/assets/Project.toml new file mode 100644 index 00000000..f7d5dbc0 --- /dev/null +++ b/docs/build/assets/Project.toml @@ -0,0 +1,15 @@ +[deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" +HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" +MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5" +Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" 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diff --git a/docs/pages.jl b/docs/pages.jl index 5b779c72..ce58fbd5 100644 --- a/docs/pages.jl +++ b/docs/pages.jl @@ -2,7 +2,9 @@ pages = ["index.md", "Getting Started" => "getting_started.md", - #"Tutorials" => Any[], + "Tutorials" => Any[ + "tutorials/resting_state_wb.md" + ], #"Manual" => Any[], "API" => "api.md", "Release Notes" => "release_notes.md", diff --git a/docs/src/assets/Project.toml b/docs/src/assets/Project.toml new file mode 100644 index 00000000..f7d5dbc0 --- /dev/null +++ b/docs/src/assets/Project.toml @@ -0,0 +1,15 @@ +[deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa" +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" +HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" +MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5" +Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2" +Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" + +[compat] +Documenter = "1" diff --git a/docs/src/data/tract_lengths.csv b/docs/src/data/tract_lengths.csv new file mode 100644 index 00000000..2e604325 --- /dev/null +++ b/docs/src/data/tract_lengths.csv @@ -0,0 +1,76 @@ 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diff --git a/docs/src/tutorials/resting_state_wb.md b/docs/src/tutorials/resting_state_wb.md new file mode 100644 index 00000000..b2d6e474 --- /dev/null +++ b/docs/src/tutorials/resting_state_wb.md @@ -0,0 +1,78 @@ +# [Tutorial on resting state simulation using neural mass models](@id resting_state_tutorial) + +This tutorial will introduce you to simulating resting state brain dynamics using Neuroblox. + +## Building the a whole brain FitzHugh-Nagumo neural mass model + +The FitzHugh-Nagumo model is described by the follwoing equations: + +```math + \begin{align} + \dot{V} &= d \, \tau (-f V^3 + e V^2 + \alpha W - \gamma I_{c} + \sigma w(t) ) \\ + \dot{W} &= \dfrac{d}{\tau}\,\,(b V - \beta W + a + \sigma w(t) ) + \end{align} +``` + +We start by building the resting state circuit from individual Generic2dOscillator Blox + +```@tutorial resting-state-circuit +using Neuroblox +using CSV +using DataFrames +using MetaGraphs +using DifferentialEquations +using Random +using Plots +using Statistics +using HypothesisTests + +# read connection matrix from file +weights = CSV.read("../data/weights.csv",DataFrame) +region_names = names(weights) + +wm = Array(weights) + +# assemble list of neural mass models +blocks = [] +for i in 1:size(wm)[1] + push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]),bn=sqrt(5e-4))) +end + +# add neural mass models to Graph and connect using the connection matrix +g = MetaDiGraph() +add_blox!.(Ref(g), blocks) +create_adjacency_edges!(g, wm) +``` + +```@example resting-state-circuit +@named sys = system_from_graph(g) +sys = structural_simplify(sys) +``` + +To solve the system, we first create an Stochastic Differential Equation Problem and then solve it over the tspan of (0,6e) using a EulerHeun solver. The solution is saved every 0.5ms. The unit of time in Neuroblox is 1ms. + +```@example resting-state-circuit +prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 6e5), []) +sol = solve(prob, EulerHeun(), dt=0.5) +plot(sol.t,sol[5,:],xlims=(0,10000)) +``` +To evaluate the connectivity of our simulated resting state network, we calculate the statistically significant correlations + +```@example resting-state-circuit +cs = [] +for i in 1:Int((length(sol.t)-1)/1000)-1 + solv = Array(sol[1:2:end,(i-1)*1000+1:(i*1000)])' + push!(cs,cor(solv)) +end +css = stack(cs) + +p = zeros(76,76) +for i in 1:76 + for j in 1:76 + p[i,j] = pvalue(OneSampleTTest(css[i,j,:])) + end +end + +plot(heatmap(wm,aspect_ratio = :equal), heatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)) +``` +Fig. Left: Connection Adjacency Matrix, Right: log10(p value) displaying statistally significant correlation between time series From 487960098248c2d150e3e9c6912118b227bb0280 Mon Sep 17 00:00:00 2001 From: Helmut Strey Date: Tue, 4 Jun 2024 15:56:05 -0400 Subject: [PATCH 19/25] tutorial should work now --- docs/build/.documenter-siteinfo.json | 1 + docs/build/api/index.html | 123 ++ docs/build/assets/documenter.js | 1050 +++++++++++++++++ docs/build/assets/themes/documenter-dark.css | 7 + docs/build/assets/themes/documenter-light.css | 9 + docs/build/assets/themeswap.js | 84 ++ docs/build/assets/warner.js | 52 + docs/build/getting_started/263d8ea0.svg | 76 ++ docs/build/getting_started/eb7fc47d.svg | 54 + docs/build/getting_started/index.html | 124 ++ docs/build/index.html | 7 + docs/build/objects.inv | Bin 0 -> 1431 bytes docs/build/release_notes/index.html | 2 + docs/build/search_index.js | 3 + .../tutorials/resting_state_wb/55124ff9.svg | 548 +++++++++ .../tutorials/resting_state_wb/d50d58a0.svg | 382 ++++++ .../tutorials/resting_state_wb/dca3652a.svg | 48 + .../tutorials/resting_state_wb/index.html | 47 + docs/src/tutorials/resting_state_wb.md | 14 +- 19 files changed, 2626 insertions(+), 5 deletions(-) create mode 100644 docs/build/.documenter-siteinfo.json create mode 100644 docs/build/api/index.html create mode 100644 docs/build/assets/documenter.js create mode 100644 docs/build/assets/themes/documenter-dark.css create mode 100644 docs/build/assets/themes/documenter-light.css create mode 100644 docs/build/assets/themeswap.js create mode 100644 docs/build/assets/warner.js create mode 100644 docs/build/getting_started/263d8ea0.svg create mode 100644 docs/build/getting_started/eb7fc47d.svg create mode 100644 docs/build/getting_started/index.html create mode 100644 docs/build/index.html create mode 100644 docs/build/objects.inv create mode 100644 docs/build/release_notes/index.html create mode 100644 docs/build/search_index.js create mode 100644 docs/build/tutorials/resting_state_wb/55124ff9.svg create mode 100644 docs/build/tutorials/resting_state_wb/d50d58a0.svg create mode 100644 docs/build/tutorials/resting_state_wb/dca3652a.svg create mode 100644 docs/build/tutorials/resting_state_wb/index.html diff --git a/docs/build/.documenter-siteinfo.json b/docs/build/.documenter-siteinfo.json new file mode 100644 index 00000000..0558c4cd --- /dev/null +++ b/docs/build/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-06-04T15:54:29","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/docs/build/api/index.html b/docs/build/api/index.html new file mode 100644 index 00000000..74b8bcba --- /dev/null +++ b/docs/build/api/index.html @@ -0,0 +1,123 @@ + +API · Neuroblox

    GitHub

    API Documentation

    Neuroblox.BalloonModel — Type

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • lnκ: logarithmic prefactor to signal decay H[1], set to 0 for standard parameter value.
    • lnτ: logarithmic prefactor to transit time H[3], set to 0 for standard parameter value.
    • lnϵ: logarithm of ratio of intra- to extra-vascular signal

    NB: the prefix ln of the variables u, ν, q as well as the parameters κ, τ denotes their transformation into logarithmic space to enforce their positivity. This transformation is considered in the derivates of the model equations below.

    Citations:

    1. Stephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040
    2. Hofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407
    source
    Neuroblox.Generic2dOscillator — Type
    Generic2dOscillator(name, namespace, ...)
+
+The Generic2dOscillator model is a generic dynamic system with two state
+variables. The dynamic equations of this model are composed of two ordinary
+differential equations comprising two nullclines. The first nullcline is a
+cubic function as it is found in most neuron and population models; the
+second nullcline is arbitrarily configurable as a polynomial function up to
+second order. The manipulation of the latter nullcline's parameters allows
+to generate a wide range of different behaviours.
+
+Equations:
+
+```math
+        \begin{align}
+        \dot{V} &= d \, \tau (-f V^3 + e V^2 + g V + \alpha W + \gamma I) \\
+        \dot{W} &= \dfrac{d}{	au}\,\,(c V^2 + b V - \beta W + a)
+        \end{align}
+```

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • Other parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds.

    Citations: FitzHugh, R., Impulses and physiological states in theoretical models of nerve membrane, Biophysical Journal 1: 445, 1961.

    Nagumo et.al, An Active Pulse Transmission Line Simulating Nerve Axon, Proceedings of the IRE 50: 2061, 1962.

    Stefanescu, R., Jirsa, V.K. Reduced representations of heterogeneous mixed neural networks with synaptic coupling. Physical Review E, 83, 2011.

    Jirsa VK, Stefanescu R. Neural population modes capture biologically realistic large-scale network dynamics. Bulletin of Mathematical Biology, 2010.

    Stefanescu, R., Jirsa, V.K. A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons. PLoS Computational Biology, 4(11), 2008).

    source
    Neuroblox.HarmonicOscillator — Type
    HarmonicOscillator(name, namespace, ω, ζ, k, h)
+
+Create a harmonic oscillator blox with the specified parameters.
+The formal definition of this blox is:

    \[\frac{dx}{dt} = y-(2*\omega*\zeta*x)+ k*(2/\pi)*(atan((\sum{jcn})/h) +\frac{dy}{dt} = -(\omega^2)*x\]

    where ``jcn`` is any input to the blox.

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • ω: Base frequency. Note the default value is scaled to give oscillations in milliseconds to match other blocks.
    • ζ: Damping ratio.
    • k: Gain.
    • h: Threshold.
    source
    Neuroblox.JansenRit — Type
    JansenRit(name, namespace, τ, H, λ, r, cortical, delayed)
+
+Create a Jansen Rit blox as described in Liu et al.
+The formal definition of this blox is:

    \[\frac{dx}{dt} = y-\frac{2}{\tau}x +\frac{dy}{dt} = -\frac{x}{\tau^2} + \frac{H}{\tau} [\frac{2\lambda}{1+\text{exp}(-r*\sum{jcn})} - \lambda]\]

    where $jcn$ is any input to the blox.

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • τ: Time constant. This is changed from the original source as the time constant was in seconds, while all our blocks are in milliseconds.
    • H: See equation for use.
    • λ: See equation for use.
    • r: See equation for use.
    • cortical: Boolean to determine whether to use cortical or subcortical parameters. Specifying any of the parameters above will override this.
    • delayed: Boolean to indicate whether states are delayed

    Citations:

    1. Liu C, Zhou C, Wang J, Fietkiewicz C, Loparo KA. The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations. Neural Netw. 2020 Mar;123:381-392. doi: 10.1016/j.neunet.2019.12.021.
    source
    Neuroblox.LarterBreakspear — Type
    LarterBreakspear(name, namespace, ...)
+
+Create a Larter Breakspear blox described in Endo et al. For a full list of the parameters used see the reference.
+If you need to modify the parameters, see Chesebro et al. and van Nieuwenhuizen et al. for physiological ranges.

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • Other parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds.

    Citations:

    1. Endo H, Hiroe N, Yamashita O. Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates. Front Comput Neurosci. 2020 Jan 17;13:91. doi: 10.3389/fncom.2019.00091.
    2. Chesebro AG, Mujica-Parodi LR, Weistuch C. Ion gradient-driven bifurcations of a multi-scale neuronal model. Chaos Solitons Fractals. 2023 Feb;167:113120. doi: 10.1016/j.chaos.2023.113120.
    3. van Nieuwenhuizen, H, Chesebro, AG, Polis, C, Clarke, K, Strey, HH, Weistuch, C, Mujica-Parodi, LR. Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age. Preprint. bioRxiv 2023.05.10.540257; doi: https://doi.org/10.1101/2023.05.10.540257.
    source
    Neuroblox.OUBlox — Type

    Ornstein-Uhlenbeck process Blox

    variables: x(t): value jcn: input parameters: τ: relaxation time μ: average value σ: random noise (variance of OU process is τ*σ^2/2) returns: an ODE System (but with brownian parameters)

    source
    Neuroblox.OUCouplingBlox — Type

    Ornstein-Uhlenbeck Coupling Blox This blox takes an input and multiplies that input with a OU process of mean μ and variance τ*σ^2/2

    This blox allows to create edges that have fluctuating weights

    variables: x(t): value jcn: input parameters: τ: relaxation time μ: average value σ: random noise (variance of OU process is τ*σ^2/2) returns: an ODE System (but with brownian parameters)

    source
    Neuroblox.WilsonCowan — Type
    WilsonCowan(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)
+
+Create a standard Wilson Cowan blox.
+The formal definition of this blox is:

    \[\frac{dE}{dt} = \frac{-E}{\tau_E} + \frac{1}{1 + \text{exp}(-a_E*(c_{EE}*E - c_{IE}*I - \theta_E + \eta*(\sum{jcn}))} +\frac{dI}{dt} = \frac{-I}{\tau_I} + \frac{1}{1 + exp(-a_I*(c_{EI}*E - c_{II}*I - \theta_I)}\]

    where $jcn$ is any input to the blox.

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • namespace: Additional namespace above name if needed for inheritance.
    • Others: See equation for use.
    source
    Neuroblox.WinnerTakeAllBlox — Type
    WinnerTakeAllBlox

    Creates a winner-take-all local circuit found in neocortex, typically 5 pyramidal (excitatory) neurons send synapses to a single interneuron (inhibitory) and receive feedback inhibition from that interneuron.

    source
    LinearAlgebra.eigen — Method
    function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np}
+
+Dispatch of LinearAlgebra.eigen for dual matrices with complex numbers. Make the eigenvalue decomposition 
+amenable to automatic differentiation. To do so compute the analytical derivative of eigenvalues
+and eigenvectors. 
+
+Arguments:
+- `M`: matrix of type Dual of which to compute the eigenvalue decomposition. 
+
+Returns:
+- `Eigen(evals, evecs)`: eigenvalue decomposition returned as type LinearAlgebra.Eigen
    source
    Neuroblox.ARVTarget — Method

    ARVTarget Time series data is bandpass filtered and then the power spectrum is computed for a given time interval (control bin), returned as the average value of the power spectral density within a certain frequency band ([lb, ub]).

    source
    Neuroblox.CDVTarget — Method

    CDVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians. Circular difference is quantified as the angle of circular_location.

    source
    Neuroblox.PDVTarget — Method

    PDVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians. Phase deviation is quantified as the angle difference between a given set of signals.

    source
    Neuroblox.PLVTarget — Method

    PLVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians.

    source
    Neuroblox.addnontunableparams — Method
    function addnontunableparams(param, model)
+
+Function adds parameters of a model that were not marked as tunable to a list of tunable parameters
+and respects the MTK ordering of parameters.
+
+Arguments:
+- `paramlist`: parameters of an MTK system that were tagged as tunable
+- `sys`: MTK system
+
+Returns:
+- `completeparamlist`: complete parameter list of a system, including those that were not tagged as tunable
    source
    Neuroblox.bandpassfilter — Method

    bandpassfilter takes in time series data and bandpass filters it. It has the following inputs: data: time series data lb: minimum cut-off frequency ub: maximum cut-off frequency fs: sampling frequency order: filter order

    source
    Neuroblox.boldsignal — Method

    Arguments:

    • name: Name given to ODESystem object within the blox.
    • lnϵ : logarithm of ratio of intra- to extra-vascular signal

    NB: the prefix ln of the variables ν, q as well as the parameters ϵ denotes their transformation into logarithmic space to enforce their positivity.

    Citations:

    1. Stephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040
    2. Hofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407
    source
    Neuroblox.complexwavelet — Function

    complexwavelet creates a complex morlet wavelet by windowing a complex sine wave with a Gaussian taper. The morlet wavelet is a special case of a bandpass filter in which the frequency response is Gaussian-shaped. Convolution with a complex wavelet is equivalent to performing a Hilbert transform of a bandpass filtered signal.

    It has the following inputs: data: time series data dt : data sampling rate lb : lower bound wavelet frequency (in Hz) ub : upper bound wavelet frequency (in Hz) a : amplitude of the Gaussian taper, default is 1 n : number of wavelet cycles of the Gaussian taper, defines the trade-off between temporal precision and frequency precision larger n gives better frequency precision at the cost of temporal precision default is 6 Hz m : x-axis offset, default is 0 num_wavelets : number of wavelets to create, default is 5

    And outputs: complex_wavelet : a family of complex morlet wavelets

    source
    Neuroblox.csd2mar — Method

    This function converts a cross-spectral density (CSD) into a multivariate auto-regression (MAR) model. It first transforms the CSD into its cross-correlation function (Wiener-Kinchine theorem) and then computes the MAR model coefficients. csd : cross-spectral density matrix of size MxN; M: number of samples, N: number of cross-spectral dimensions (number of variables squared) w : frequencies dt : time step size p : number of time steps of auto-regressive model

    This function returns coeff : array of length p of coefficient matrices of size sqrt(N)xsqrt(N) noise_cov : noise covariance matrix

    source
    Neuroblox.csd_approx — Method
    This function implements equation 2 of the spectral DCM paper, Friston et al. 2014 "A DCM for resting state fMRI".
+Note that nomenclature is taken from SPM12 code and it does not seem to coincide with the spectral DCM paper's nomenclature. 
+For instance, Gu should represent the spectral component due to external input according to the paper. However, in the code this represents
+the hidden state fluctuations (which are called Gν in the paper).
+Gn in the code corresponds to Ge in the paper, i.e. the observation noise. In the code global and local components are defined, no such distinction
+is discussed in the paper. In fact the parameter γ, corresponding to local component is not present in the paper.
    source
    Neuroblox.get_dynamic_states — Method
    function get_dynamic_states(sys)
+
+Function extracts states from the system that are dynamic variables, 
+get also indices of external inputs (u(t)) and measurements (like bold(t))
+Arguments:
+- `sys`: MTK system
+
+Returns:
+- `sts`  : states of the system that are neither external inputs nor measurements, i.e. these are the dynamic states
+- `idx_u`: indices of states that represent external inputs
+- `idx_m`: indices of states that represent measurements
    source
    Neuroblox.idft — Method

    Plain implementation of idft because AD dispatch versions for ifft don't work still!

    source
    Neuroblox.inner_namespaceof — Method
    Returns the complete namespace EXCLUDING the outermost (highest) level.
+This is useful for manually preparing equations (e.g. connections, see BloxConnector),
+that will later be composed and will automatically get the outermost namespace.
    source
    Neuroblox.input_equations — Method
    Returns the equations for all input variables of a system, 
+assuming they have a form like : `sys.input_variable ~ ...`
+so only the input appears on the LHS.
+
+Input equations are namespaced by the inner namespace of blox
+and then they are returned. This way during system `compose` downstream,
+the higher-level namespaces will be added to them.
+
+If blox isa AbstractComponent, it is assumed that it contains a `connector` field,
+which holds a `BloxConnector` object with all relevant connections 
+from lower levels and this level.
    source
    Neuroblox.learningrate — Method

    This function computes learning rate. It has the following inputs: outcomes: vector of 1's and 0's for behavioral outcomes windows: number of windows to split the outcome data into And the following outputs: rate: the learning rate across each window

    source
    Neuroblox.mar2csd — Method

    This function converts multivariate auto-regression (MAR) model parameters to a cross-spectral density (CSD). A : coefficients of MAR model, array of length p, each element contains the regression coefficients for that particular time-lag. Σ : noise covariance matrix of MAR p : number of time lags freqs : frequencies at which to evaluate the CSD sf : sampling frequency

    This function returns: csd : cross-spectral density matrix of size MxN; M: number of samples, N: number of cross-spectral dimensions (number of variables squared)

    source
    Neuroblox.mar_ml — Method

    Maximum likelihood estimator of a multivariate, or vector auto-regressive model. y : MxN Data matrix where M is number of samples and N is number of dimensions p : time lag parameter, also called order of MAR model return values mar["A"] : model parameters is a NxNxP tensor, i.e. one NxN parameter matrix for each time bin k ∈ {1,...,p} mar["Σ"] : noise covariance matrix

    source
    Neuroblox.matlab_norm — Method
    function matlab_norm(A, p)
+
+Simple helper function to implement the norm of a matrix that is equivalent to the one given in MATLAB for order=1, 2, Inf. 
+This is needed for the reproduction of the exact same results of SPM12.
+
+Arguments:
+- `A`: matrix
+- `p`: order of norm
    source
    Neuroblox.paramscoping — Method
    function paramscoping(;kwargs...)
+
+Scope arguments that are already a symbolic model parameter thereby keep the correct namespace 
+and make those that are not yet symbolic a symbol.
+Keyword arguments are used, because parameter definition require names, not just values.
    source
    Neuroblox.phase_cos_blox — Method

    phasecosblox is creating a cos with angular frequency ω and variable phase phaseinter has the following parameters: ω: angular frequency t: time phaseinter: a function that returns phase as a function of time and returns: the resulting value

    Usage: phaseint = phaseinter(0:0.1:50,phasedata) phaseout(t) = phasecosblox(0.1,t,phaseint) which is now a function of time and can be used in an input blox you can also use the dot operator to calculate time-series signal = phaseout.(collect(0:0.01:50))

    source
    Neuroblox.phase_inter — Method

    phaseinter is creating a function that interpolates the phase data for any time given phaseinter has the following parameters: phaserange: a range, e.g. 0:0.1:50 which should reflect the time points of the data phasedata: phase at equidistant time points and returns: an function that returns an interpolated phase for t in range

    source
    Neuroblox.phase_sin_blox — Method

    phasesinblox is creating a sin with angular frequency ω and variable phase phaseinter has the following parameters: ω: angular frequency t: time phaseinter: a function that returns phase as a function of time and returns: the resulting value

    Usage: phaseint = phaseinter(0:0.1:50,phasedata) phaseout(t) = phasesinblox(0.1,t,phaseint) which is now a function of time and can be used in an input blox you can also use the dot operator to calculate time-series signal = phaseout.(collect(0:0.01:50))

    source
    Neuroblox.random_initials — Method

    random_initials creates a vector of random initial conditions for an ODESystem that is composed of a list of blox. The function finds the initial conditions in the blox and then sets a random value in between range tuple given for that state.

    It has the following inputs: odesys: ODESystem blox : list of blox

    And outputs: u0 : Float64 vector of initial conditions

    source
    Neuroblox.setup_sDCM — Method
    function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, params_idx)
+
+Interface function to performs variational inference to fit model parameters to empirical cross spectral density.
+The current implementation provides a Variational Laplace fit (see function above `variationalbayes`).
+
+Arguments:
+- `data`        : dataframe with column names corresponding to the regions of measurement.
+- `model`       : MTK model, including state evolution and measurement.
+- `initcond`    : dictionary of initial conditions, numerical values for all states
+- `csdsetup`    : dictionary of parameters required for the computation of the cross spectral density
+-- `dt`         : sampling interval
+-- `freq`       : frequencies at which to evaluate the CSD
+-- `p`          : order parameter of the multivariate autoregression model
+- `priors`      : dataframe of parameters with the following columns:
+-- `name`       : corresponds to MTK model name
+-- `mean`       : corresponds to prior mean value
+-- `variance`   : corresponds to the prior variances
+- `hyperpriors` : dataframe of parameters with the following columns:
+-- `Πλ_pr`      : prior precision matrix for λ hyperparameter(s)
+-- `μλ_pr`      : prior mean(s) for λ hyperparameter(s)
+- `params_idx`  : indices to separate model parameters from other parameters. Needed for the computation of AD gradient.
    source
    Neuroblox.spm_logdet — Method
    function spm_logdet(M)
+
+SPM12 style implementation of the logarithm of the determinant of a matrix.
+
+Arguments:
+- `M`: matrix
    source
    Neuroblox.vecparam — Method
    vecparam(param::OrderedDict)
+
+Function to flatten an ordered dictionary of model parameters and return a simple list of parameter values.
+
+Arguments:
+- `param`: dictionary of model parameters (may contain numbers and lists of numbers)
    source
    diff --git a/docs/build/assets/documenter.js b/docs/build/assets/documenter.js new file mode 100644 index 00000000..c6562b55 --- /dev/null +++ b/docs/build/assets/documenter.js @@ -0,0 +1,1050 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function (event) { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + let animationSpeed = event.noToggleAnimation ? 0 : 400; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(animationSpeed); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(animationSpeed); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let meta = $("div[data-docstringscollapsed]").data(); + + if (meta?.docstringscollapsed) { + $("#documenter-article-toggle-button").trigger({ + type: "click", + noToggleAnimation: true, + }); + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +/* +To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +PSEUDOCODE: + +Searching happens automatically as the user types or adjusts the selected filters. +To preserve responsiveness, as much as possible of the slow parts of the search are done +in a web worker. Searching and result generation are done in the worker, and filtering and +DOM updates are done in the main thread. The filters are in the main thread as they should +be very quick to apply. This lets filters be changed without re-searching with minisearch +(which is possible even if filtering is on the worker thread) and also lets filters be +changed _while_ the worker is searching and without message passing (neither of which are +possible if filtering is on the worker thread) + +SEARCH WORKER: + +Import minisearch + +Build index + +On message from main thread + run search + find the first 200 unique results from each category, and compute their divs for display + note that this is necessary and sufficient information for the main thread to find the + first 200 unique results from any given filter set + post results to main thread + +MAIN: + +Launch worker + +Declare nonconstant globals (worker_is_running, last_search_text, unfiltered_results) + +On text update + if worker is not running, launch_search() + +launch_search + set worker_is_running to true, set last_search_text to the search text + post the search query to worker + +on message from worker + if last_search_text is not the same as the text in the search field, + the latest search result is not reflective of the latest search query, so update again + launch_search() + otherwise + set worker_is_running to false + + regardless, display the new search results to the user + save the unfiltered_results as a global + update_search() + +on filter click + adjust the filter selection + update_search() + +update_search + apply search filters by looping through the unfiltered_results and finding the first 200 + unique results that match the filters + + Update the DOM +*/ + +/////// SEARCH WORKER /////// + +function worker_function(documenterSearchIndex, documenterBaseURL, filters) { + importScripts( + "https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min.js" + ); + + let data = documenterSearchIndex.map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; + }); + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", + ]); + + let index = new MiniSearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + + word = word.toLowerCase(); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not + // find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + prefix: true, + boost: { title: 100 }, + fuzzy: 2, + }, + }); + + index.addAll(data); + + /** + * Used to map characters to HTML entities. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const htmlEscapes = { + "&": "&", + "<": "<", + ">": ">", + '"': """, + "'": "'", + }; + + /** + * Used to match HTML entities and HTML characters. + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + const reUnescapedHtml = /[&<>"']/g; + const reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** + * Escape function from lodash + * Refer: https://github.com/lodash/lodash/blob/main/src/escape.ts + */ + function escape(string) { + return string && reHasUnescapedHtml.test(string) + ? string.replace(reUnescapedHtml, (chr) => htmlEscapes[chr]) + : string || ""; + } + + /** + * Make the result component given a minisearch result data object and the value + * of the search input as queryString. To view the result object structure, refer: + * https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ + function make_search_result(result, querystring) { + let search_divider = `
    `; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`${querystring}`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + text = text.length ? escape(text) : ""; + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`${escape(querystring)}`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
    +
    ${escape(result.title)}
    +
    ${result.category}
    +
    +

    + ${display_result} +

    +
    + ${display_link} +
    +     + ${search_divider} + `; + + return result_div; + } + + self.onmessage = function (e) { + let query = e.data; + let results = index.search(query, { + filter: (result) => { + // Only return relevant results + return result.score >= 1; + }, + }); + + // Pre-filter to deduplicate and limit to 200 per category to the extent + // possible without knowing what the filters are. + let filtered_results = []; + let counts = {}; + for (let filter of filters) { + counts[filter] = 0; + } + let present = {}; + + for (let result of results) { + cat = result.category; + cnt = counts[cat]; + if (cnt < 200) { + id = cat + "---" + result.location; + if (present[id]) { + continue; + } + present[id] = true; + filtered_results.push({ + location: result.location, + category: cat, + div: make_search_result(result, query), + }); + } + } + + postMessage(filtered_results); + }; +} + +// `worker = Threads.@spawn worker_function(documenterSearchIndex)`, but in JavaScript! +const filters = [ + ...new Set(documenterSearchIndex["docs"].map((x) => x.category)), +]; +const worker_str = + "(" + + worker_function.toString() + + ")(" + + JSON.stringify(documenterSearchIndex["docs"]) + + "," + + JSON.stringify(documenterBaseURL) + + "," + + JSON.stringify(filters) + + ")"; +const worker_blob = new Blob([worker_str], { type: "text/javascript" }); +const worker = new Worker(URL.createObjectURL(worker_blob)); + +/////// SEARCH MAIN /////// + +// Whether the worker is currently handling a search. This is a boolean +// as the worker only ever handles 1 or 0 searches at a time. +var worker_is_running = false; + +// The last search text that was sent to the worker. This is used to determine +// if the worker should be launched again when it reports back results. +var last_search_text = ""; + +// The results of the last search. This, in combination with the state of the filters +// in the DOM, is used compute the results to display on calls to update_search. +var unfiltered_results = []; + +// Which filter is currently selected +var selected_filter = ""; + +$(document).on("input", ".documenter-search-input", function (event) { + if (!worker_is_running) { + launch_search(); + } +}); + +function launch_search() { + worker_is_running = true; + last_search_text = $(".documenter-search-input").val(); + worker.postMessage(last_search_text); +} + +worker.onmessage = function (e) { + if (last_search_text !== $(".documenter-search-input").val()) { + launch_search(); + } else { + worker_is_running = false; + } + + unfiltered_results = e.data; + update_search(); +}; + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + selected_filter = ""; + } else { + selected_filter = $(this).text().toLowerCase(); + } + + // This updates search results and toggles classes for UI: + update_search(); +}); + +/** + * Make/Update the search component + */ +function update_search() { + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + if (selected_filter == "") { + results = unfiltered_results; + } else { + results = unfiltered_results.filter((result) => { + return selected_filter == result.category.toLowerCase(); + }); + } + + let search_result_container = ``; + let modal_filters = make_modal_body_filters(); + let search_divider = `
    `; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + for (var i = 0, n = results.length; i < n && count < 200; ++i) { + let result = results[i]; + if (result.location && !links.includes(result.location)) { + search_results += result.div; + count++; + links.push(result.location); + } + } + + if (count == 1) { + count_str = "1 result"; + } else if (count == 200) { + count_str = "200+ results"; + } else { + count_str = count + " results"; + } + let result_count = `
    ${count_str}
    `; + + search_result_container = ` +
    + ${modal_filters} + ${search_divider} + ${result_count} +
    + ${search_results} +
    +
    + `; + } else { + search_result_container = ` +
    + ${modal_filters} + ${search_divider} +
    0 result(s)
    +
    +
    No result found!
    + `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(` +
    Type something to get started!
    + `); + } +} + +/** + * Make the modal filter html + * + * @returns string + */ +function make_modal_body_filters() { + let str = filters + .map((val) => { + if (selected_filter == val.toLowerCase()) { + return `${val}`; + } else { + return `${val}`; + } + }) + .join(""); + + return ` +
    + Filters: + ${str} +
    `; +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +$(document).ready(function () { + let search_modal_header = ` +
    +
    +

    + + + + +

    +
    +
    + +
    +
    + `; + + let initial_search_body = ` +
    Type something to get started!
    + `; + + let search_modal_footer = ` +
    + + Ctrl + + / to search + + esc to close +
    + `; + + $(document.body).append( + ` + + ` + ); + + document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); + }); + + document + .querySelector(".close-search-modal") + .addEventListener("click", () => { + closeModal(); + }); + + $(document).on("click", ".search-result-link", function () { + closeModal(); + }); + + document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; + }); + + // Functions to open and close a modal + function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); + } + + function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
    Type something to get started!
    + `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); + } + + document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/docs/build/assets/themes/documenter-dark.css b/docs/build/assets/themes/documenter-dark.css new file mode 100644 index 00000000..1d717015 --- /dev/null +++ b/docs/build/assets/themes/documenter-dark.css @@ 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.pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark fieldset[disabled] .pagination-previous,fieldset[disabled] html.theme--documenter-dark .pagination-next,html.theme--documenter-dark fieldset[disabled] .pagination-next,fieldset[disabled] html.theme--documenter-dark .pagination-link,html.theme--documenter-dark fieldset[disabled] .pagination-link,fieldset[disabled] html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark fieldset[disabled] .pagination-ellipsis,fieldset[disabled] html.theme--documenter-dark .file-cta,html.theme--documenter-dark fieldset[disabled] .file-cta,fieldset[disabled] html.theme--documenter-dark .file-name,html.theme--documenter-dark fieldset[disabled] .file-name,fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar 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.button.is-white.is-inverted.is-hovered{background-color:#000}html.theme--documenter-dark .button.is-white.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted{background-color:#0a0a0a;border-color:transparent;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-white.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-outlined.is-focused{background-color:#fff;border-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-outlined.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-white.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-focused{background-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-white.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-white.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-white.is-inverted.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:hover,html.theme--documenter-dark .button.is-black.is-hovered{background-color:#040404;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus,html.theme--documenter-dark .button.is-black.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black:focus:not(:active),html.theme--documenter-dark .button.is-black.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(10,10,10,0.25)}html.theme--documenter-dark .button.is-black:active,html.theme--documenter-dark .button.is-black.is-active{background-color:#000;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-black[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black{background-color:#0a0a0a;border-color:#0a0a0a;box-shadow:none}html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-focused{background-color:#fff;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:hover,html.theme--documenter-dark .button.is-light.is-hovered{background-color:#e5eaec;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus,html.theme--documenter-dark .button.is-light.is-focused{border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light:focus:not(:active),html.theme--documenter-dark .button.is-light.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(236,240,241,0.25)}html.theme--documenter-dark .button.is-light:active,html.theme--documenter-dark .button.is-light.is-active{background-color:#dde4e6;border-color:transparent;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light{background-color:#ecf0f1;border-color:#ecf0f1;box-shadow:none}html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-hovered{background-color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted{background-color:rgba(0,0,0,0.7);border-color:transparent;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-loading::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-outlined.is-focused{background-color:#ecf0f1;border-color:#ecf0f1;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-outlined.is-loading::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-outlined.is-loading.is-focused::after{border-color:transparent transparent rgba(0,0,0,0.7) rgba(0,0,0,0.7) !important}html.theme--documenter-dark .button.is-light.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-outlined{background-color:transparent;border-color:#ecf0f1;box-shadow:none;color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-focused{background-color:rgba(0,0,0,0.7);color:#ecf0f1}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-light.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ecf0f1 #ecf0f1 !important}html.theme--documenter-dark .button.is-light.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-light.is-inverted.is-outlined{background-color:transparent;border-color:rgba(0,0,0,0.7);box-shadow:none;color:rgba(0,0,0,0.7)}html.theme--documenter-dark .button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark .button.is-dark:active,html.theme--documenter-dark .content kbd.button:active,html.theme--documenter-dark .button.is-dark.is-active,html.theme--documenter-dark .content kbd.button.is-active{background-color:#1d2122;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark[disabled],html.theme--documenter-dark .content kbd.button[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark,fieldset[disabled] html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:#282f2f;box-shadow:none}html.theme--documenter-dark .button.is-dark.is-inverted,html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted:hover,html.theme--documenter-dark .content kbd.button.is-inverted:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-outlined.is-focused{background-color:#282f2f;border-color:#282f2f;color:#fff}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined:focus,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-focused,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-focused{background-color:#fff;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus:not(:active),html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus:not(:active),html.theme--documenter-dark .button.is-primary.is-focused:not(:active),html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink:not(:active){box-shadow:0 0 0 0.125em rgba(55,90,127,0.25)}html.theme--documenter-dark .button.is-primary:active,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:active,html.theme--documenter-dark .button.is-primary.is-active,html.theme--documenter-dark .docstring>section>a.button.is-active.docs-sourcelink{background-color:#2f4d6d;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary[disabled],html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-primary,fieldset[disabled] html.theme--documenter-dark 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.button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fff7de;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fff3d1;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark 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.button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 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+ Maintainer: @highlightjs/core-team + Website: https://highlightjs.org/ + License: see project LICENSE + Touched: 2021 +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/docs/build/assets/themeswap.js b/docs/build/assets/themeswap.js new file mode 100644 index 00000000..9f5eebe6 --- /dev/null +++ b/docs/build/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } + } + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/docs/build/assets/warner.js b/docs/build/assets/warner.js new file mode 100644 index 00000000..3f6f5d00 --- /dev/null +++ b/docs/build/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. 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    GitHub

    Getting Started with Neuroblox

    This tutorial will introduce you to simulating brain dynamics using Neuroblox.

    Example 1 : Building an oscillating circuit from two Wilson-Cowan Neural Mass Models

    The Wilson–Cowan model describes the dynamics of interactions between populations of excitatory and inhibitory neurons. Each Wilson-Cowan Blox is described by the follwoing equations:

    \[\frac{dE}{dt} = \frac{-E}{\tau_E} + \frac{1}{1 + \text{exp}(-a_E*(c_{EE}*E - c_{IE}*I - \theta_E + \eta*(\sum{jcn}))}\\[10pt] +\frac{dI}{dt} = \frac{-I}{\tau_I} + \frac{1}{1 + exp(-a_I*(c_{EI}*E - c_{II}*I - \theta_I)}\]

    Our first example is to simply combine two Wilson-Cowan Blox to build an oscillatory circuit

    using Neuroblox
+using DifferentialEquations
+using Graphs
+using MetaGraphs
+using Plots
+
+@named WC1 = WilsonCowan()
+@named WC2 = WilsonCowan()
+
+g = MetaDiGraph()
+add_blox!.(Ref(g), [WC1, WC2])
+
+adj = [-1 6; 6 -1]
+create_adjacency_edges!(g, adj)

    First, we create the two Wilson-Cowan Blox: WC1 and WC2. Next, we add the two Blox into a directed graph as nodes and then we are creating weighted edges between the two nodes using an adjacency matrix.

    Now we are ready to build the ModelingToolkit System. Structural simplify creates the final set of equations in which all substiutions are made.

    @named sys = system_from_graph(g)
+sys = structural_simplify(sys)

    \[ \begin{align} +\frac{\mathrm{d} WC1_{+}E\left( t \right)}{\mathrm{d}t} =& \frac{ - WC1_{+}E\left( t \right)}{WC1_+\tau_{E}} + \frac{1}{1 + e^{ - WC1_{+}a_{E} \left( - WC1_+\theta_{E} + WC1_{+}c_{EE} WC1_{+}E\left( t \right) - WC1_{+}c_{IE} WC1_{+}I\left( t \right) + WC1_+\eta WC1_{+}jcn\left( t \right) \right)}} \\ +\frac{\mathrm{d} WC1_{+}I\left( t \right)}{\mathrm{d}t} =& \frac{1}{1 + e^{ - WC1_{+}a_{I} \left( - WC1_+\theta_{I} + WC1_{+}c_{EI} WC1_{+}E\left( t \right) - WC1_{+}c_{II} WC1_{+}I\left( t \right) \right)}} + \frac{ - WC1_{+}I\left( t \right)}{WC1_+\tau_{I}} \\ +\frac{\mathrm{d} WC2_{+}E\left( t \right)}{\mathrm{d}t} =& \frac{ - WC2_{+}E\left( t \right)}{WC2_+\tau_{E}} + \frac{1}{1 + e^{ - WC2_{+}a_{E} \left( - WC2_+\theta_{E} + WC2_{+}c_{EE} WC2_{+}E\left( t \right) - WC2_{+}c_{IE} WC2_{+}I\left( t \right) + WC2_+\eta WC2_{+}jcn\left( t \right) \right)}} \\ +\frac{\mathrm{d} WC2_{+}I\left( t \right)}{\mathrm{d}t} =& \frac{ - WC2_{+}I\left( t \right)}{WC2_+\tau_{I}} + \frac{1}{1 + e^{ - WC2_{+}a_{I} \left( - WC2_+\theta_{I} + WC2_{+}c_{EI} WC2_{+}E\left( t \right) - WC2_{+}c_{II} WC2_{+}I\left( t \right) \right)}} +\end{align} + \]

    To solve the system, we first create an ODEProblem and then solve it over the tspan of (0,100) using a stiff solver. The solution is saved every 0.1ms. The unit of time in Neuroblox is 1ms.

    prob = ODEProblem(sys, [], (0.0, 100), [])
+sol = solve(prob, Rodas4(), saveat=0.1)
+plot(sol)
    Example block output

    Example 2 : Building a Brain Circuit from literature using Neural Mass Models

    In this example, we will construct a Parkinsons model from eight Jansen-Rit Neural Mass Models as described in Liu et al. (2020). DOI: 10.1016/j.neunet.2019.12.021. The Jansen-Rit Neural Mass model is defined by the following differential equations:

    \[\frac{dx}{dt} = y-\frac{2}{\tau}x +\frac{dy}{dt} = -\frac{x}{\tau^2} + \frac{H}{\tau} [\frac{2\lambda}{1+\text{exp}(-r*\sum{jcn})} - \lambda]\]

    using Neuroblox
+using DifferentialEquations
+using Graphs
+using MetaGraphs
+using Plots

    The original paper units are in seconds we therefore need to multiply all parameters with a common factor

    τ_factor = 1000
+
+@named Str = JansenRit(τ=0.0022*τ_factor, H=20/τ_factor, λ=300, r=0.3)
+@named GPE = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ
+@named STN = JansenRit(τ=0.01*τ_factor, H=20/τ_factor, λ=500, r=0.1)
+@named GPI = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
+@named Th  = JansenRit(τ=0.002*τ_factor, H=10/τ_factor, λ=20, r=5)
+@named EI  = JansenRit(τ=0.01*τ_factor, H=20/τ_factor, λ=5, r=5)
+@named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
+@named II  = JansenRit(τ=2.0*τ_factor, H=60/τ_factor, λ=5, r=5)
+blox = [Str, GPE, STN, GPI, Th, EI, PY, II]
    8-element Vector{JansenRit}:
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb4, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :Str, ODESystem[], Dict{Any, Any}(τ => 2.2, H => 0.02, x(t) => 1.0, λ => 300, r => 0.3, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb5, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :GPE, ODESystem[], Dict{Any, Any}(τ => 40.0, H => 0.02, x(t) => 1.0, λ => 400.0, r => 0.1, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb6, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :STN, ODESystem[], Dict{Any, Any}(τ => 10.0, H => 0.02, x(t) => 1.0, λ => 500, r => 0.1, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb7, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :GPI, ODESystem[], Dict{Any, Any}(τ => 14, H => 0.02, x(t) => 1.0, λ => 400.0, r => 0.1, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb8, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :Th, ODESystem[], Dict{Any, Any}(τ => 2.0, H => 0.01, x(t) => 1.0, λ => 20, r => 5, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fb9, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :EI, ODESystem[], Dict{Any, Any}(τ => 10.0, H => 0.02, x(t) => 1.0, λ => 5, r => 5, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fba, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :PY, ODESystem[], Dict{Any, Any}(τ => 1, H => 0.02, x(t) => 1.0, λ => 5.0, r => 0.15, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)
+ JansenRit(Any[τ, H, λ, r], x(t), jcn(t), ODESystem(0x0000000000003fbb, Equation[Differential(t)(x(t)) ~ y(t) + (-2x(t)) / τ, Differential(t)(y(t)) ~ (-x(t)) / (τ^2) + (H*((2λ) / (1 + exp(-r*jcn(t))) - λ)) / τ], t, SymbolicUtils.BasicSymbolic{Real}[x(t), y(t), jcn(t)], SymbolicUtils.BasicSymbolic{Real}[τ, H, λ, r], nothing, Dict{Any, Any}(:H => H, :y => y(t), :λ => λ, :τ => τ, :jcn => jcn(t), :r => r, :x => x(t)), Any[], Equation[], Base.RefValue{Vector{Num}}(Num[]), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), Base.RefValue{Matrix{Num}}(Matrix{Num}(undef, 0, 0)), :II, ODESystem[], Dict{Any, Any}(τ => 2000.0, H => 0.06, x(t) => 1.0, λ => 5, r => 5, jcn(t) => 0.0, y(t) => 1.0), Dict{Any, Any}(), nothing, nothing, Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[], nothing, nothing, nothing, nothing, nothing, false, nothing, nothing, nothing, nothing, nothing), nothing)

    Again, we create a graph and add the Blox as nodes

    g = MetaDiGraph()
+add_blox!.(Ref(g), blox)
+
+params = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5

    \[ \begin{equation} +\left[ +\begin{array}{c} +C_{Cor} \\ +C_{BG\_Th} \\ +C_{Cor\_BG\_Th} \\ +C_{BG\_Th\_Cor} \\ +\end{array} +\right] +\end{equation} + \]

    ModelingToolkit allows us to create parameters that can be passed into the equations symbolically.

    We add edges as specified in Table 2 of Liu et al. We only implemented a subset of the nodes and edges to describe a less complex version of the model. Edges can also be created using an adjacency matrix as in the previous example.

    add_edge!(g, 2, 1, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 2, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 2, 3, Dict(:weight => C_BG_Th))
+add_edge!(g, 3, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 3, 7, Dict(:weight => C_Cor_BG_Th))
+add_edge!(g, 4, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 4, 3, Dict(:weight => C_BG_Th))
+add_edge!(g, 5, 4, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 6, 5, Dict(:weight => C_BG_Th_Cor))
+add_edge!(g, 6, 7, Dict(:weight => 6*C_Cor))
+add_edge!(g, 7, 6, Dict(:weight => 4.8*C_Cor))
+add_edge!(g, 7, 8, Dict(:weight => -1.5*C_Cor))
+add_edge!(g, 8, 7, Dict(:weight => 1.5*C_Cor))
+add_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))
+add_edge!(g,1,1,:weight, -0.5*C_BG_Th)
+add_edge!(g,1,2,:weight, C_BG_Th)
+add_edge!(g,2,1,:weight, -0.5*C_BG_Th)
+add_edge!(g,2,5,:weight, C_Cor_BG_Th)
+add_edge!(g,3,1,:weight, -0.5*C_BG_Th)
+add_edge!(g,3,2,:weight, C_BG_Th)
+add_edge!(g,4,3,:weight, -0.5*C_BG_Th)
+add_edge!(g,4,4,:weight, C_BG_Th_Cor)
    true

    Now we are ready to build the ModelingToolkit System and apply structural simplification to the equations.

    @named final_system = system_from_graph(g)
+final_system_sys = structural_simplify(final_system)

    \[ \begin{align} +\frac{\mathrm{d} Str_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 Str_{+}x\left( t \right)}{Str_+\tau} + Str_{+}y\left( t \right) \\ +\frac{\mathrm{d} Str_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{ - Str_{+}x\left( t \right)}{Str_+\tau^{2}} + \frac{Str_{+}H \left( - Str_+\lambda + \frac{2 Str_+\lambda}{1 + e^{ - Str_{+}r Str_{+}jcn\left( t \right)}} \right)}{Str_+\tau} \\ +\frac{\mathrm{d} GPE_{+}x\left( t \right)}{\mathrm{d}t} =& GPE_{+}y\left( t \right) + \frac{ - 2 GPE_{+}x\left( t \right)}{GPE_+\tau} \\ +\frac{\mathrm{d} GPE_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{GPE_{+}H \left( - GPE_+\lambda + \frac{2 GPE_+\lambda}{1 + e^{ - GPE_{+}r GPE_{+}jcn\left( t \right)}} \right)}{GPE_+\tau} + \frac{ - GPE_{+}x\left( t \right)}{GPE_+\tau^{2}} \\ +\frac{\mathrm{d} STN_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 STN_{+}x\left( t \right)}{STN_+\tau} + STN_{+}y\left( t \right) \\ +\frac{\mathrm{d} STN_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{STN_{+}H \left( - STN_+\lambda + \frac{2 STN_+\lambda}{1 + e^{ - STN_{+}r STN_{+}jcn\left( t \right)}} \right)}{STN_+\tau} + \frac{ - STN_{+}x\left( t \right)}{STN_+\tau^{2}} \\ +\frac{\mathrm{d} GPI_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 GPI_{+}x\left( t \right)}{GPI_+\tau} + GPI_{+}y\left( t \right) \\ +\frac{\mathrm{d} GPI_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{ - GPI_{+}x\left( t \right)}{GPI_+\tau^{2}} + \frac{GPI_{+}H \left( - GPI_+\lambda + \frac{2 GPI_+\lambda}{1 + e^{ - GPI_{+}r GPI_{+}jcn\left( t \right)}} \right)}{GPI_+\tau} \\ +\frac{\mathrm{d} Th_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 Th_{+}x\left( t \right)}{Th_+\tau} + Th_{+}y\left( t \right) \\ +\frac{\mathrm{d} Th_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{Th_{+}H \left( - Th_+\lambda + \frac{2 Th_+\lambda}{1 + e^{ - Th_{+}r Th_{+}jcn\left( t \right)}} \right)}{Th_+\tau} + \frac{ - Th_{+}x\left( t \right)}{Th_+\tau^{2}} \\ +\frac{\mathrm{d} EI_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 EI_{+}x\left( t \right)}{EI_+\tau} + EI_{+}y\left( t \right) \\ +\frac{\mathrm{d} EI_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{ - EI_{+}x\left( t \right)}{EI_+\tau^{2}} + \frac{EI_{+}H \left( - EI_+\lambda + \frac{2 EI_+\lambda}{1 + e^{ - EI_{+}r EI_{+}jcn\left( t \right)}} \right)}{EI_+\tau} \\ +\frac{\mathrm{d} PY_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 PY_{+}x\left( t \right)}{PY_+\tau} + PY_{+}y\left( t \right) \\ +\frac{\mathrm{d} PY_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{PY_{+}H \left( - PY_+\lambda + \frac{2 PY_+\lambda}{1 + e^{ - PY_{+}r PY_{+}jcn\left( t \right)}} \right)}{PY_+\tau} + \frac{ - PY_{+}x\left( t \right)}{PY_+\tau^{2}} \\ +\frac{\mathrm{d} II_{+}x\left( t \right)}{\mathrm{d}t} =& \frac{ - 2 II_{+}x\left( t \right)}{II_+\tau} + II_{+}y\left( t \right) \\ +\frac{\mathrm{d} II_{+}y\left( t \right)}{\mathrm{d}t} =& \frac{ - II_{+}x\left( t \right)}{II_+\tau^{2}} + \frac{II_{+}H \left( - II_+\lambda + \frac{2 II_+\lambda}{1 + e^{ - II_{+}r II_{+}jcn\left( t \right)}} \right)}{II_+\tau} +\end{align} + \]

    Our Jansen-Rit model allows delayed edges, and we therefore need to collect those delays (in our case all delays are zero). Then we build a Delayed Differential Equations Problem (DDEProblem).

    sim_dur = 1000.0 # Simulate for 1 second
+prob = ODEProblem(final_system_sys,
+    [],
+    (0.0, sim_dur))
    ODEProblem with uType Vector{Float64} and tType Float64. In-place: true
+timespan: (0.0, 1000.0)
+u0: 16-element Vector{Float64}:
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0
+ 1.0

    We select an algorithm and solve the system

    alg = Tsit5()
+sol_dde_no_delays = solve(prob, alg, saveat=1)
+plot(sol_dde_no_delays)
    Example block output

    In a later tutorial, we will show how to introduce edge delays.

    diff --git a/docs/build/index.html b/docs/build/index.html new file mode 100644 index 00000000..c95f1d58 --- /dev/null +++ b/docs/build/index.html @@ -0,0 +1,7 @@ + +Neuroblox · Neuroblox
    GitHub

    Neuroblox

    About

    Neuroblox.jl is designed for computational neuroscience and psychiatry applications. Our tools range from control circuit system identification to brain circuit simulations bridging scales from spiking neurons to fMRI-derived circuits, parameter-fitting models to neuroimaging data, interactions between the brain and other physiological systems, experimental optimization, and scientific machine learning.

    Description

    Neuroblox.jl is based on a library of modular computational building blocks (“blox”) in the form of systems of symbolic dynamic differential equations that can be combined to describe large-scale brain dynamics. Once a model is built, it can be simulated efficiently and fit electrophysiological and neuroimaging data. Moreover, the circuit behavior of multiple model variants can be investigated to aid in distinguishing between competing hypotheses. We employ ModelingToolkit.jl to describe the dynamical behavior of blox as symbolic (stochastic/delay) differential equations. Our libraries of modular blox consist of individual neurons (Hodgkin-Huxley, IF, QIF, LIF, etc.), neural mass models (Jansen-Rit, Wilson-Cowan, Lauter-Breakspear, Next Generation, microcanonical circuits etc.) and biomimetically-constrained control circuit elements. A GUI designed to be intuitive to neuroscientists allows researchers to build models that automatically generate high-performance systems of numerical ordinary/stochastic differential equations from which one can run stimulations with parameters fit to experimental data. Our benchmarks show that the increase in speed for simulation often exceeds a factor of 100 as compared to neural mass model implementation by the Virtual Brain (python) and similar packages in MATLAB. For parameter fitting of brain circuit dynamical models, we use Turing.jl to perform probabilistic modeling, including Hamilton-Monte-Carlo sampling and Automated Differentiation Variational Inference.

    Installation

    To install Neuroblox.jl, first add the JuliaHubRegistry and then use the Julia package manager:

    using Pkg
+Pkg.add("PkgAuthentication")
+using PkgAuthentication
+PkgAuthentication.install("juliahub.com")
+Pkg.Registry.add()
+Pkg.add("Neuroblox")

    Licensing

    Neuroblox is free for non-commerical and academic use. For full details of the license, please see the Neuroblox EULA. For commercial use, get in contact with sales@neuroblox.org.

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    GitHub
    diff --git a/docs/build/search_index.js b/docs/build/search_index.js new file mode 100644 index 00000000..34e9095c --- /dev/null +++ b/docs/build/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"release_notes/#Release-Notes","page":"Release Notes","title":"Release Notes","text":"","category":"section"},{"location":"release_notes/#v0.3","page":"Release Notes","title":"v0.3","text":"","category":"section"},{"location":"release_notes/","page":"Release Notes","title":"Release Notes","text":"Initial Release!","category":"page"},{"location":"api/#API-Documentation","page":"API","title":"API Documentation","text":"","category":"section"},{"location":"api/","page":"API","title":"API","text":"Modules = [Neuroblox]","category":"page"},{"location":"api/#Neuroblox.BalloonModel","page":"API","title":"Neuroblox.BalloonModel","text":"Arguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nlnκ: logarithmic prefactor to signal decay H[1], set to 0 for standard parameter value.\nlnτ: logarithmic prefactor to transit time H[3], set to 0 for standard parameter value.\nlnϵ: logarithm of ratio of intra- to extra-vascular signal\n\nNB: the prefix ln of the variables u, ν, q as well as the parameters κ, τ denotes their transformation into logarithmic space to enforce their positivity. This transformation is considered in the derivates of the model equations below. \n\nCitations:\n\nStephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040\nHofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.Generic2dOscillator","page":"API","title":"Neuroblox.Generic2dOscillator","text":"Generic2dOscillator(name, namespace, ...)\n\nThe Generic2dOscillator model is a generic dynamic system with two state\nvariables. The dynamic equations of this model are composed of two ordinary\ndifferential equations comprising two nullclines. The first nullcline is a\ncubic function as it is found in most neuron and population models; the\nsecond nullcline is arbitrarily configurable as a polynomial function up to\nsecond order. The manipulation of the latter nullcline's parameters allows\nto generate a wide range of different behaviours.\n\nEquations:\n\n```math\n \\begin{align}\n \\dot{V} &= d \\, \\tau (-f V^3 + e V^2 + g V + \\alpha W + \\gamma I) \\\\\n \\dot{W} &= \\dfrac{d}{\tau}\\,\\,(c V^2 + b V - \\beta W + a)\n \\end{align}\n```\n\nArguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nOther parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds.\n\nCitations: FitzHugh, R., Impulses and physiological states in theoretical models of nerve membrane, Biophysical Journal 1: 445, 1961.\n\nNagumo et.al, An Active Pulse Transmission Line Simulating Nerve Axon, Proceedings of the IRE 50: 2061, 1962.\n\nStefanescu, R., Jirsa, V.K. Reduced representations of heterogeneous mixed neural networks with synaptic coupling. Physical Review E, 83, 2011.\n\nJirsa VK, Stefanescu R. Neural population modes capture biologically realistic large-scale network dynamics. Bulletin of Mathematical Biology, 2010.\n\nStefanescu, R., Jirsa, V.K. A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons. PLoS Computational Biology, 4(11), 2008).\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.HarmonicOscillator","page":"API","title":"Neuroblox.HarmonicOscillator","text":"HarmonicOscillator(name, namespace, ω, ζ, k, h)\n\nCreate a harmonic oscillator blox with the specified parameters.\nThe formal definition of this blox is:\n\nfracdxdt = y-(2*omega*zeta*x)+ k*(2pi)*(atan((sumjcn)h)\nfracdydt = -(omega^2)*x\n\nwhere ``jcn`` is any input to the blox.\n\nArguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nω: Base frequency. Note the default value is scaled to give oscillations in milliseconds to match other blocks.\nζ: Damping ratio.\nk: Gain.\nh: Threshold.\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.JansenRit","page":"API","title":"Neuroblox.JansenRit","text":"JansenRit(name, namespace, τ, H, λ, r, cortical, delayed)\n\nCreate a Jansen Rit blox as described in Liu et al.\nThe formal definition of this blox is:\n\nfracdxdt = y-frac2taux\nfracdydt = -fracxtau^2 + fracHtau frac2lambda1+textexp(-r*sumjcn) - lambda\n\nwhere jcn is any input to the blox.\n\nArguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nτ: Time constant. This is changed from the original source as the time constant was in seconds, while all our blocks are in milliseconds.\nH: See equation for use.\nλ: See equation for use.\nr: See equation for use.\ncortical: Boolean to determine whether to use cortical or subcortical parameters. Specifying any of the parameters above will override this.\ndelayed: Boolean to indicate whether states are delayed\n\nCitations:\n\nLiu C, Zhou C, Wang J, Fietkiewicz C, Loparo KA. The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations. Neural Netw. 2020 Mar;123:381-392. doi: 10.1016/j.neunet.2019.12.021.\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.JansenRitSPM12","page":"API","title":"Neuroblox.JansenRitSPM12","text":"Jansen-Rit model block for canonical micro circuit, analogous to the implementation in SPM12\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.LarterBreakspear","page":"API","title":"Neuroblox.LarterBreakspear","text":"LarterBreakspear(name, namespace, ...)\n\nCreate a Larter Breakspear blox described in Endo et al. For a full list of the parameters used see the reference.\nIf you need to modify the parameters, see Chesebro et al. and van Nieuwenhuizen et al. for physiological ranges.\n\nArguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nOther parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds.\n\nCitations:\n\nEndo H, Hiroe N, Yamashita O. Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates. Front Comput Neurosci. 2020 Jan 17;13:91. doi: 10.3389/fncom.2019.00091.\nChesebro AG, Mujica-Parodi LR, Weistuch C. Ion gradient-driven bifurcations of a multi-scale neuronal model. Chaos Solitons Fractals. 2023 Feb;167:113120. doi: 10.1016/j.chaos.2023.113120. \nvan Nieuwenhuizen, H, Chesebro, AG, Polis, C, Clarke, K, Strey, HH, Weistuch, C, Mujica-Parodi, LR. Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age. Preprint. bioRxiv 2023.05.10.540257; doi: https://doi.org/10.1101/2023.05.10.540257. \n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.OUBlox","page":"API","title":"Neuroblox.OUBlox","text":"Ornstein-Uhlenbeck process Blox\n\nvariables: x(t): value jcn: input parameters: τ: relaxation time \tμ: average value \tσ: random noise (variance of OU process is τ*σ^2/2) returns: an ODE System (but with brownian parameters)\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.OUCouplingBlox","page":"API","title":"Neuroblox.OUCouplingBlox","text":"Ornstein-Uhlenbeck Coupling Blox This blox takes an input and multiplies that input with a OU process of mean μ and variance τ*σ^2/2\n\nThis blox allows to create edges that have fluctuating weights\n\nvariables: x(t): value jcn: input parameters: τ: relaxation time \tμ: average value \tσ: random noise (variance of OU process is τ*σ^2/2) returns: an ODE System (but with brownian parameters)\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.Striatum","page":"API","title":"Neuroblox.Striatum","text":"Subcortical blox\nall subcprtical blox used in cortico-striatal model are defined here\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.WilsonCowan","page":"API","title":"Neuroblox.WilsonCowan","text":"WilsonCowan(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)\n\nCreate a standard Wilson Cowan blox.\nThe formal definition of this blox is:\n\nfracdEdt = frac-Etau_E + frac11 + textexp(-a_E*(c_EE*E - c_IE*I - theta_E + eta*(sumjcn))\nfracdIdt = frac-Itau_I + frac11 + exp(-a_I*(c_EI*E - c_II*I - theta_I)\n\nwhere jcn is any input to the blox.\n\nArguments:\n\nname: Name given to ODESystem object within the blox.\nnamespace: Additional namespace above name if needed for inheritance.\nOthers: See equation for use.\n\n\n\n\n\n","category":"type"},{"location":"api/#Neuroblox.WinnerTakeAllBlox","page":"API","title":"Neuroblox.WinnerTakeAllBlox","text":"WinnerTakeAllBlox\n\nCreates a winner-take-all local circuit found in neocortex, typically 5 pyramidal (excitatory) neurons send synapses to a single interneuron (inhibitory) and receive feedback inhibition from that interneuron.\n\n\n\n\n\n","category":"type"},{"location":"api/#LinearAlgebra.eigen-Union{Tuple{Array{ForwardDiff.Dual{T, P, np}, 2}}, Tuple{np}, Tuple{P}, Tuple{T}} where {T, P, np}","page":"API","title":"LinearAlgebra.eigen","text":"function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np}\n\nDispatch of LinearAlgebra.eigen for dual matrices with complex numbers. Make the eigenvalue decomposition \namenable to automatic differentiation. To do so compute the analytical derivative of eigenvalues\nand eigenvectors. \n\nArguments:\n- `M`: matrix of type Dual of which to compute the eigenvalue decomposition. \n\nReturns:\n- `Eigen(evals, evecs)`: eigenvalue decomposition returned as type LinearAlgebra.Eigen\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.ARVTarget-NTuple{6, Any}","page":"API","title":"Neuroblox.ARVTarget","text":"ARVTarget Time series data is bandpass filtered and then the power spectrum is computed for a given time interval (control bin), returned as the average value of the power spectral density within a certain frequency band ([lb, ub]).\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.CDVTarget-NTuple{5, Any}","page":"API","title":"Neuroblox.CDVTarget","text":"CDVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians. Circular difference is quantified as the angle of circular_location.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.ControlError-NTuple{8, Any}","page":"API","title":"Neuroblox.ControlError","text":"ControlError Returns the control error (deviation of the actual value from the target value).\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.PDVTarget-NTuple{5, Any}","page":"API","title":"Neuroblox.PDVTarget","text":"PDVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians. Phase deviation is quantified as the angle difference between a given set of signals.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.PLVTarget-NTuple{6, Any}","page":"API","title":"Neuroblox.PLVTarget","text":"PLVTarget Time series data is bandpass filtered and hilbert-transformed. Phase angle is computed in radians.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.addnontunableparams-Tuple{Any, Any}","page":"API","title":"Neuroblox.addnontunableparams","text":"function addnontunableparams(param, model)\n\nFunction adds parameters of a model that were not marked as tunable to a list of tunable parameters\nand respects the MTK ordering of parameters.\n\nArguments:\n- `paramlist`: parameters of an MTK system that were tagged as tunable\n- `sys`: MTK system\n\nReturns:\n- `completeparamlist`: complete parameter list of a system, including those that were not tagged as tunable\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.bandpassfilter-Tuple{}","page":"API","title":"Neuroblox.bandpassfilter","text":"bandpassfilter takes in time series data and bandpass filters it. It has the following inputs: data: time series data lb: minimum cut-off frequency ub: maximum cut-off frequency fs: sampling frequency order: filter order\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.boldsignal-Tuple{}","page":"API","title":"Neuroblox.boldsignal","text":"Arguments:\n\nname: Name given to ODESystem object within the blox.\nlnϵ : logarithm of ratio of intra- to extra-vascular signal\n\nNB: the prefix ln of the variables ν, q as well as the parameters ϵ denotes their transformation into logarithmic space to enforce their positivity.\n\nCitations:\n\nStephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040\nHofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.complexwavelet","page":"API","title":"Neuroblox.complexwavelet","text":"complexwavelet creates a complex morlet wavelet by windowing a complex sine wave with a Gaussian taper. The morlet wavelet is a special case of a bandpass filter in which the frequency response is Gaussian-shaped. Convolution with a complex wavelet is equivalent to performing a Hilbert transform of a bandpass filtered signal.\n\nIt has the following inputs: data: time series data dt : data sampling rate lb : lower bound wavelet frequency (in Hz) ub : upper bound wavelet frequency (in Hz) a : amplitude of the Gaussian taper, default is 1 n : number of wavelet cycles of the Gaussian taper, defines the trade-off between temporal precision and frequency precision larger n gives better frequency precision at the cost of temporal precision default is 6 Hz m : x-axis offset, default is 0 num_wavelets : number of wavelets to create, default is 5\n\nAnd outputs: complex_wavelet : a family of complex morlet wavelets\n\n\n\n\n\n","category":"function"},{"location":"api/#Neuroblox.csd2mar-NTuple{4, Any}","page":"API","title":"Neuroblox.csd2mar","text":"This function converts a cross-spectral density (CSD) into a multivariate auto-regression (MAR) model. It first transforms the CSD into its cross-correlation function (Wiener-Kinchine theorem) and then computes the MAR model coefficients. csd : cross-spectral density matrix of size MxN; M: number of samples, N: number of cross-spectral dimensions (number of variables squared) w : frequencies dt : time step size p : number of time steps of auto-regressive model\n\nThis function returns coeff : array of length p of coefficient matrices of size sqrt(N)xsqrt(N) noise_cov : noise covariance matrix\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.csd_approx-NTuple{4, Any}","page":"API","title":"Neuroblox.csd_approx","text":"This function implements equation 2 of the spectral DCM paper, Friston et al. 2014 \"A DCM for resting state fMRI\".\nNote that nomenclature is taken from SPM12 code and it does not seem to coincide with the spectral DCM paper's nomenclature. \nFor instance, Gu should represent the spectral component due to external input according to the paper. However, in the code this represents\nthe hidden state fluctuations (which are called Gν in the paper).\nGn in the code corresponds to Ge in the paper, i.e. the observation noise. In the code global and local components are defined, no such distinction\nis discussed in the paper. In fact the parameter γ, corresponding to local component is not present in the paper.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.get_dynamic_states-Tuple{Any}","page":"API","title":"Neuroblox.get_dynamic_states","text":"function get_dynamic_states(sys)\n\nFunction extracts states from the system that are dynamic variables, \nget also indices of external inputs (u(t)) and measurements (like bold(t))\nArguments:\n- `sys`: MTK system\n\nReturns:\n- `sts` : states of the system that are neither external inputs nor measurements, i.e. these are the dynamic states\n- `idx_u`: indices of states that represent external inputs\n- `idx_m`: indices of states that represent measurements\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.idft-Tuple{AbstractArray}","page":"API","title":"Neuroblox.idft","text":"Plain implementation of idft because AD dispatch versions for ifft don't work still!\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.inner_namespaceof-Tuple{Any}","page":"API","title":"Neuroblox.inner_namespaceof","text":"Returns the complete namespace EXCLUDING the outermost (highest) level.\nThis is useful for manually preparing equations (e.g. connections, see BloxConnector),\nthat will later be composed and will automatically get the outermost namespace.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.input_equations-Tuple{Any}","page":"API","title":"Neuroblox.input_equations","text":"Returns the equations for all input variables of a system, \nassuming they have a form like : `sys.input_variable ~ ...`\nso only the input appears on the LHS.\n\nInput equations are namespaced by the inner namespace of blox\nand then they are returned. This way during system `compose` downstream,\nthe higher-level namespaces will be added to them.\n\nIf blox isa AbstractComponent, it is assumed that it contains a `connector` field,\nwhich holds a `BloxConnector` object with all relevant connections \nfrom lower levels and this level.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.learningrate-Tuple{Any, Any}","page":"API","title":"Neuroblox.learningrate","text":"This function computes learning rate. It has the following inputs: outcomes: vector of 1's and 0's for behavioral outcomes windows: number of windows to split the outcome data into And the following outputs: rate: the learning rate across each window\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.mar2csd-Tuple{Any, Any, Any}","page":"API","title":"Neuroblox.mar2csd","text":"This function converts multivariate auto-regression (MAR) model parameters to a cross-spectral density (CSD). A : coefficients of MAR model, array of length p, each element contains the regression coefficients for that particular time-lag. Σ : noise covariance matrix of MAR p : number of time lags freqs : frequencies at which to evaluate the CSD sf : sampling frequency\n\nThis function returns: csd : cross-spectral density matrix of size MxN; M: number of samples, N: number of cross-spectral dimensions (number of variables squared)\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.mar_ml-Tuple{Any, Any}","page":"API","title":"Neuroblox.mar_ml","text":"Maximum likelihood estimator of a multivariate, or vector auto-regressive model. y : MxN Data matrix where M is number of samples and N is number of dimensions p : time lag parameter, also called order of MAR model return values mar[\"A\"] : model parameters is a NxNxP tensor, i.e. one NxN parameter matrix for each time bin k ∈ {1,...,p} mar[\"Σ\"] : noise covariance matrix\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.matlab_norm-Tuple{Any, Any}","page":"API","title":"Neuroblox.matlab_norm","text":"function matlab_norm(A, p)\n\nSimple helper function to implement the norm of a matrix that is equivalent to the one given in MATLAB for order=1, 2, Inf. \nThis is needed for the reproduction of the exact same results of SPM12.\n\nArguments:\n- `A`: matrix\n- `p`: order of norm\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.params-Tuple{Neuroblox.BloxConnector}","page":"API","title":"Neuroblox.params","text":"Helper to merge delays and weights into a single vector\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.paramscoping-Tuple{}","page":"API","title":"Neuroblox.paramscoping","text":"function paramscoping(;kwargs...)\n\nScope arguments that are already a symbolic model parameter thereby keep the correct namespace \nand make those that are not yet symbolic a symbol.\nKeyword arguments are used, because parameter definition require names, not just values.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.phase_cos_blox-Union{Tuple{F}, Tuple{Any, Any, F}} where F","page":"API","title":"Neuroblox.phase_cos_blox","text":"phasecosblox is creating a cos with angular frequency ω and variable phase phaseinter has the following parameters: ω: angular frequency t: time phaseinter: a function that returns phase as a function of time and returns: the resulting value\n\nUsage: phaseint = phaseinter(0:0.1:50,phasedata) phaseout(t) = phasecosblox(0.1,t,phaseint) which is now a function of time and can be used in an input blox you can also use the dot operator to calculate time-series signal = phaseout.(collect(0:0.01:50))\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.phase_inter-Tuple{Any, Any}","page":"API","title":"Neuroblox.phase_inter","text":"phaseinter is creating a function that interpolates the phase data for any time given phaseinter has the following parameters: phaserange: a range, e.g. 0:0.1:50 which should reflect the time points of the data phasedata: phase at equidistant time points and returns: an function that returns an interpolated phase for t in range\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.phase_sin_blox-Union{Tuple{F}, Tuple{Any, Any, F}} where F","page":"API","title":"Neuroblox.phase_sin_blox","text":"phasesinblox is creating a sin with angular frequency ω and variable phase phaseinter has the following parameters: ω: angular frequency t: time phaseinter: a function that returns phase as a function of time and returns: the resulting value\n\nUsage: phaseint = phaseinter(0:0.1:50,phasedata) phaseout(t) = phasesinblox(0.1,t,phaseint) which is now a function of time and can be used in an input blox you can also use the dot operator to calculate time-series signal = phaseout.(collect(0:0.01:50))\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.phaseangle-Tuple{}","page":"API","title":"Neuroblox.phaseangle","text":"phaseangle takes in time series data, hilbert transforms it, and estimates the phase angle.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.random_initials-Tuple{ODESystem, Any}","page":"API","title":"Neuroblox.random_initials","text":"random_initials creates a vector of random initial conditions for an ODESystem that is composed of a list of blox. The function finds the initial conditions in the blox and then sets a random value in between range tuple given for that state.\n\nIt has the following inputs: odesys: ODESystem blox : list of blox\n\nAnd outputs: u0 : Float64 vector of initial conditions\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.sample_affect!-NTuple{4, Any}","page":"API","title":"Neuroblox.sample_affect!","text":"Non-symbolic, time-block-based way of `@register_symbolic sample_poisson(λ)`.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.setup_sDCM-NTuple{7, Any}","page":"API","title":"Neuroblox.setup_sDCM","text":"function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, params_idx)\n\nInterface function to performs variational inference to fit model parameters to empirical cross spectral density.\nThe current implementation provides a Variational Laplace fit (see function above `variationalbayes`).\n\nArguments:\n- `data` : dataframe with column names corresponding to the regions of measurement.\n- `model` : MTK model, including state evolution and measurement.\n- `initcond` : dictionary of initial conditions, numerical values for all states\n- `csdsetup` : dictionary of parameters required for the computation of the cross spectral density\n-- `dt` : sampling interval\n-- `freq` : frequencies at which to evaluate the CSD\n-- `p` : order parameter of the multivariate autoregression model\n- `priors` : dataframe of parameters with the following columns:\n-- `name` : corresponds to MTK model name\n-- `mean` : corresponds to prior mean value\n-- `variance` : corresponds to the prior variances\n- `hyperpriors` : dataframe of parameters with the following columns:\n-- `Πλ_pr` : prior precision matrix for λ hyperparameter(s)\n-- `μλ_pr` : prior mean(s) for λ hyperparameter(s)\n- `params_idx` : indices to separate model parameters from other parameters. Needed for the computation of AD gradient.\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.spm_logdet-Tuple{Any}","page":"API","title":"Neuroblox.spm_logdet","text":"function spm_logdet(M)\n\nSPM12 style implementation of the logarithm of the determinant of a matrix.\n\nArguments:\n- `M`: matrix\n\n\n\n\n\n","category":"method"},{"location":"api/#Neuroblox.vecparam-Tuple{OrderedCollections.OrderedDict}","page":"API","title":"Neuroblox.vecparam","text":"vecparam(param::OrderedDict)\n\nFunction to flatten an ordered dictionary of model parameters and return a simple list of parameter values.\n\nArguments:\n- `param`: dictionary of model parameters (may contain numbers and lists of numbers)\n\n\n\n\n\n","category":"method"},{"location":"getting_started/#neuroblox_example","page":"Getting Started","title":"Getting Started with Neuroblox","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"This tutorial will introduce you to simulating brain dynamics using Neuroblox.","category":"page"},{"location":"getting_started/#Example-1-:-Building-an-oscillating-circuit-from-two-Wilson-Cowan-Neural-Mass-Models","page":"Getting Started","title":"Example 1 : Building an oscillating circuit from two Wilson-Cowan Neural Mass Models","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"The Wilson–Cowan model describes the dynamics of interactions between populations of excitatory and inhibitory neurons. Each Wilson-Cowan Blox is described by the follwoing equations:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"fracdEdt = frac-Etau_E + frac11 + textexp(-a_E*(c_EE*E - c_IE*I - theta_E + eta*(sumjcn))10pt\nfracdIdt = frac-Itau_I + frac11 + exp(-a_I*(c_EI*E - c_II*I - theta_I)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Our first example is to simply combine two Wilson-Cowan Blox to build an oscillatory circuit","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using Neuroblox\nusing DifferentialEquations\nusing Graphs\nusing MetaGraphs\nusing Plots\n\n@named WC1 = WilsonCowan()\n@named WC2 = WilsonCowan()\n\ng = MetaDiGraph()\nadd_blox!.(Ref(g), [WC1, WC2])\n\nadj = [-1 6; 6 -1]\ncreate_adjacency_edges!(g, adj)\n","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"First, we create the two Wilson-Cowan Blox: WC1 and WC2. Next, we add the two Blox into a directed graph as nodes and then we are creating weighted edges between the two nodes using an adjacency matrix.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Now we are ready to build the ModelingToolkit System. Structural simplify creates the final set of equations in which all substiutions are made.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"@named sys = system_from_graph(g)\nsys = structural_simplify(sys)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"To solve the system, we first create an ODEProblem and then solve it over the tspan of (0,100) using a stiff solver. The solution is saved every 0.1ms. The unit of time in Neuroblox is 1ms.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"prob = ODEProblem(sys, [], (0.0, 100), [])\nsol = solve(prob, Rodas4(), saveat=0.1)\nplot(sol)","category":"page"},{"location":"getting_started/#Example-2-:-Building-a-Brain-Circuit-from-literature-using-Neural-Mass-Models","page":"Getting Started","title":"Example 2 : Building a Brain Circuit from literature using Neural Mass Models","text":"","category":"section"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"In this example, we will construct a Parkinsons model from eight Jansen-Rit Neural Mass Models as described in Liu et al. (2020). DOI: 10.1016/j.neunet.2019.12.021. The Jansen-Rit Neural Mass model is defined by the following differential equations:","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"fracdxdt = y-frac2taux\nfracdydt = -fracxtau^2 + fracHtau frac2lambda1+textexp(-r*sumjcn) - lambda","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"using Neuroblox\nusing DifferentialEquations\nusing Graphs\nusing MetaGraphs\nusing Plots","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"The original paper units are in seconds we therefore need to multiply all parameters with a common factor","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"τ_factor = 1000\n\n@named Str = JansenRit(τ=0.0022*τ_factor, H=20/τ_factor, λ=300, r=0.3)\n@named GPE = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ\n@named STN = JansenRit(τ=0.01*τ_factor, H=20/τ_factor, λ=500, r=0.1)\n@named GPI = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox\n@named Th = JansenRit(τ=0.002*τ_factor, H=10/τ_factor, λ=20, r=5)\n@named EI = JansenRit(τ=0.01*τ_factor, H=20/τ_factor, λ=5, r=5)\n@named PY = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox\n@named II = JansenRit(τ=2.0*τ_factor, H=60/τ_factor, λ=5, r=5)\nblox = [Str, GPE, STN, GPI, Th, EI, PY, II]","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Again, we create a graph and add the Blox as nodes","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"g = MetaDiGraph()\nadd_blox!.(Ref(g), blox)\n\nparams = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"ModelingToolkit allows us to create parameters that can be passed into the equations symbolically.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"We add edges as specified in Table 2 of Liu et al. We only implemented a subset of the nodes and edges to describe a less complex version of the model. Edges can also be created using an adjacency matrix as in the previous example.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"add_edge!(g, 2, 1, Dict(:weight => -0.5*C_BG_Th))\nadd_edge!(g, 2, 2, Dict(:weight => -0.5*C_BG_Th))\nadd_edge!(g, 2, 3, Dict(:weight => C_BG_Th))\nadd_edge!(g, 3, 2, Dict(:weight => -0.5*C_BG_Th))\nadd_edge!(g, 3, 7, Dict(:weight => C_Cor_BG_Th))\nadd_edge!(g, 4, 2, Dict(:weight => -0.5*C_BG_Th))\nadd_edge!(g, 4, 3, Dict(:weight => C_BG_Th))\nadd_edge!(g, 5, 4, Dict(:weight => -0.5*C_BG_Th))\nadd_edge!(g, 6, 5, Dict(:weight => C_BG_Th_Cor))\nadd_edge!(g, 6, 7, Dict(:weight => 6*C_Cor))\nadd_edge!(g, 7, 6, Dict(:weight => 4.8*C_Cor))\nadd_edge!(g, 7, 8, Dict(:weight => -1.5*C_Cor))\nadd_edge!(g, 8, 7, Dict(:weight => 1.5*C_Cor))\nadd_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))\nadd_edge!(g,1,1,:weight, -0.5*C_BG_Th)\nadd_edge!(g,1,2,:weight, C_BG_Th)\nadd_edge!(g,2,1,:weight, -0.5*C_BG_Th)\nadd_edge!(g,2,5,:weight, C_Cor_BG_Th)\nadd_edge!(g,3,1,:weight, -0.5*C_BG_Th)\nadd_edge!(g,3,2,:weight, C_BG_Th)\nadd_edge!(g,4,3,:weight, -0.5*C_BG_Th)\nadd_edge!(g,4,4,:weight, C_BG_Th_Cor)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Now we are ready to build the ModelingToolkit System and apply structural simplification to the equations.","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"@named final_system = system_from_graph(g)\nfinal_system_sys = structural_simplify(final_system)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"Our Jansen-Rit model allows delayed edges, and we therefore need to collect those delays (in our case all delays are zero). Then we build a Delayed Differential Equations Problem (DDEProblem).","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"sim_dur = 1000.0 # Simulate for 1 second\nprob = ODEProblem(final_system_sys,\n [],\n (0.0, sim_dur))","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"We select an algorithm and solve the system","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"alg = Tsit5()\nsol_dde_no_delays = solve(prob, alg, saveat=1)\nplot(sol_dde_no_delays)","category":"page"},{"location":"getting_started/","page":"Getting Started","title":"Getting Started","text":"In a later tutorial, we will show how to introduce edge delays.","category":"page"},{"location":"#Neuroblox","page":"Neuroblox","title":"Neuroblox","text":"","category":"section"},{"location":"#About","page":"Neuroblox","title":"About","text":"","category":"section"},{"location":"","page":"Neuroblox","title":"Neuroblox","text":"Neuroblox.jl is designed for computational neuroscience and psychiatry applications. Our tools range from control circuit system identification to brain circuit simulations bridging scales from spiking neurons to fMRI-derived circuits, parameter-fitting models to neuroimaging data, interactions between the brain and other physiological systems, experimental optimization, and scientific machine learning.","category":"page"},{"location":"#Description","page":"Neuroblox","title":"Description","text":"","category":"section"},{"location":"","page":"Neuroblox","title":"Neuroblox","text":"Neuroblox.jl is based on a library of modular computational building blocks (“blox”) in the form of systems of symbolic dynamic differential equations that can be combined to describe large-scale brain dynamics. Once a model is built, it can be simulated efficiently and fit electrophysiological and neuroimaging data. Moreover, the circuit behavior of multiple model variants can be investigated to aid in distinguishing between competing hypotheses. We employ ModelingToolkit.jl to describe the dynamical behavior of blox as symbolic (stochastic/delay) differential equations. Our libraries of modular blox consist of individual neurons (Hodgkin-Huxley, IF, QIF, LIF, etc.), neural mass models (Jansen-Rit, Wilson-Cowan, Lauter-Breakspear, Next Generation, microcanonical circuits etc.) and biomimetically-constrained control circuit elements. A GUI designed to be intuitive to neuroscientists allows researchers to build models that automatically generate high-performance systems of numerical ordinary/stochastic differential equations from which one can run stimulations with parameters fit to experimental data. Our benchmarks show that the increase in speed for simulation often exceeds a factor of 100 as compared to neural mass model implementation by the Virtual Brain (python) and similar packages in MATLAB. For parameter fitting of brain circuit dynamical models, we use Turing.jl to perform probabilistic modeling, including Hamilton-Monte-Carlo sampling and Automated Differentiation Variational Inference.","category":"page"},{"location":"#Installation","page":"Neuroblox","title":"Installation","text":"","category":"section"},{"location":"","page":"Neuroblox","title":"Neuroblox","text":"To install Neuroblox.jl, first add the JuliaHubRegistry and then use the Julia package manager:","category":"page"},{"location":"","page":"Neuroblox","title":"Neuroblox","text":"using Pkg\nPkg.add(\"PkgAuthentication\")\nusing PkgAuthentication\nPkgAuthentication.install(\"juliahub.com\")\nPkg.Registry.add()\nPkg.add(\"Neuroblox\")","category":"page"},{"location":"#Licensing","page":"Neuroblox","title":"Licensing","text":"","category":"section"},{"location":"","page":"Neuroblox","title":"Neuroblox","text":"Neuroblox is free for non-commerical and academic use. For full details of the license, please see the Neuroblox EULA. For commercial use, get in contact with sales@neuroblox.org.","category":"page"},{"location":"tutorials/resting_state_wb/#resting_state_tutorial","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"","category":"section"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"This tutorial will introduce you to simulating resting state brain dynamics using Neuroblox.","category":"page"},{"location":"tutorials/resting_state_wb/#Building-the-a-whole-brain-FitzHugh-Nagumo-neural-mass-model","page":"Tutorial on resting state simulation using neural mass models","title":"Building the a whole brain FitzHugh-Nagumo neural mass model","text":"","category":"section"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"The FitzHugh-Nagumo model is described by the follwoing equations:","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":" beginalign\n dotV = d tau (-f V^3 + e V^2 + alpha W - gamma I_c + sigma w(t) ) \n dotW = dfracdtau(b V - beta W + a + sigma w(t) )\n endalign","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"We start by building the resting state circuit from individual Generic2dOscillator Blox","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"using Neuroblox\nusing CSV\nusing DataFrames\nusing MetaGraphs\nusing DifferentialEquations\nusing Random\nusing Plots\nusing Statistics\nusing HypothesisTests\n\n# read connection matrix from file\nweights = CSV.read(\"../data/weights.csv\",DataFrame)\nregion_names = names(weights)\n\nwm = Array(weights)\n\n# assemble list of neural mass models\nblocks = []\nfor i in 1:size(wm)[1]\n push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]),bn=sqrt(5e-4)))\nend\n\n# add neural mass models to Graph and connect using the connection matrix\ng = MetaDiGraph()\nadd_blox!.(Ref(g), blocks)\ncreate_adjacency_edges!(g, wm)","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"@named sys = system_from_graph(g)\nsys = structural_simplify(sys)\nlength(unknowns(sys))","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"To solve the system, we first create an Stochastic Differential Equation Problem and then solve it over the tspan of (0,6e) using a EulerHeun solver. The solution is saved every 0.5ms. The unit of time in Neuroblox is 1ms.","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 6e5), [])\nsol = solve(prob, EulerHeun(), dt=0.5, saveat=5)\nplot(sol.t,sol[5,:],xlims=(0,10000))","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"To evaluate the connectivity of our simulated resting state network, we calculate the statistically significant correlations","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"cs = []\nfor i in 1:Int((length(sol.t)-1)/1000)-1\n solv = Array(sol[1:2:end,(i-1)*1000+1:(i*1000)])'\n push!(cs,cor(solv))\nend\ncss = stack(cs)\n\np = zeros(76,76)\nfor i in 1:76\n for j in 1:76\n p[i,j] = pvalue(OneSampleTTest(css[i,j,:]))\n end\nend\nheatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"Fig.: log10(p value) displaying statistally significant correlation between time series","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"heatmap(wm,aspect_ratio = :equal)","category":"page"},{"location":"tutorials/resting_state_wb/","page":"Tutorial on resting state simulation using neural mass models","title":"Tutorial on resting state simulation using neural mass models","text":"Fig.: Connection Adjacency Matrix that was used to connect the neural mass models","category":"page"}] +} diff --git a/docs/build/tutorials/resting_state_wb/55124ff9.svg b/docs/build/tutorials/resting_state_wb/55124ff9.svg new file mode 100644 index 00000000..e1f1497a --- /dev/null +++ b/docs/build/tutorials/resting_state_wb/55124ff9.svg @@ -0,0 +1,548 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/build/tutorials/resting_state_wb/d50d58a0.svg b/docs/build/tutorials/resting_state_wb/d50d58a0.svg new file mode 100644 index 00000000..6092be33 --- /dev/null +++ b/docs/build/tutorials/resting_state_wb/d50d58a0.svg @@ -0,0 +1,382 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/build/tutorials/resting_state_wb/dca3652a.svg b/docs/build/tutorials/resting_state_wb/dca3652a.svg new file mode 100644 index 00000000..2ba199f4 --- /dev/null +++ b/docs/build/tutorials/resting_state_wb/dca3652a.svg @@ -0,0 +1,48 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/build/tutorials/resting_state_wb/index.html b/docs/build/tutorials/resting_state_wb/index.html new file mode 100644 index 00000000..8a6e122f --- /dev/null +++ b/docs/build/tutorials/resting_state_wb/index.html @@ -0,0 +1,47 @@ + +Tutorial on resting state simulation using neural mass models · Neuroblox
    GitHub

    Tutorial on resting state simulation using neural mass models

    This tutorial will introduce you to simulating resting state brain dynamics using Neuroblox.

    Building the a whole brain FitzHugh-Nagumo neural mass model

    The FitzHugh-Nagumo model is described by the follwoing equations:

    \[ \begin{align} + \dot{V} &= d \, \tau (-f V^3 + e V^2 + \alpha W - \gamma I_{c} + \sigma w(t) ) \\ + \dot{W} &= \dfrac{d}{\tau}\,\,(b V - \beta W + a + \sigma w(t) ) + \end{align}\]

    We start by building the resting state circuit from individual Generic2dOscillator Blox

    using Neuroblox
+using CSV
+using DataFrames
+using MetaGraphs
+using DifferentialEquations
+using Random
+using Plots
+using Statistics
+using HypothesisTests
+
+# read connection matrix from file
+weights = CSV.read("../data/weights.csv",DataFrame)
+region_names = names(weights)
+
+wm = Array(weights)
+
+# assemble list of neural mass models
+blocks = []
+for i in 1:size(wm)[1]
+    push!(blocks, Neuroblox.Generic2dOscillator(name=Symbol(region_names[i]),bn=sqrt(5e-4)))
+end
+
+# add neural mass models to Graph and connect using the connection matrix
+g = MetaDiGraph()
+add_blox!.(Ref(g), blocks)
+create_adjacency_edges!(g, wm)
    @named sys = system_from_graph(g)
+sys = structural_simplify(sys)
+length(unknowns(sys))
    152

    To solve the system, we first create an Stochastic Differential Equation Problem and then solve it over the tspan of (0,6e) using a EulerHeun solver. The solution is saved every 0.5ms. The unit of time in Neuroblox is 1ms.

    prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 6e5), [])
+sol = solve(prob, EulerHeun(), dt=0.5, saveat=5)
+plot(sol.t,sol[5,:],xlims=(0,10000))
    Example block output

    To evaluate the connectivity of our simulated resting state network, we calculate the statistically significant correlations

    cs = []
+for i in 1:Int((length(sol.t)-1)/1000)-1
+    solv = Array(sol[1:2:end,(i-1)*1000+1:(i*1000)])'
+    push!(cs,cor(solv))
+end
+css = stack(cs)
+
+p = zeros(76,76)
+for i in 1:76
+    for j in 1:76
+        p[i,j] = pvalue(OneSampleTTest(css[i,j,:]))
+    end
+end
+heatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)
    Example block output

    Fig.: log10(p value) displaying statistally significant correlation between time series

    heatmap(wm,aspect_ratio = :equal)
    Example block output

    Fig.: Connection Adjacency Matrix that was used to connect the neural mass models

    diff --git a/docs/src/tutorials/resting_state_wb.md b/docs/src/tutorials/resting_state_wb.md index b2d6e474..67194dff 100644 --- a/docs/src/tutorials/resting_state_wb.md +++ b/docs/src/tutorials/resting_state_wb.md @@ -15,7 +15,7 @@ The FitzHugh-Nagumo model is described by the follwoing equations: We start by building the resting state circuit from individual Generic2dOscillator Blox -```@tutorial resting-state-circuit +```@example resting-state-circuit using Neuroblox using CSV using DataFrames @@ -47,13 +47,14 @@ create_adjacency_edges!(g, wm) ```@example resting-state-circuit @named sys = system_from_graph(g) sys = structural_simplify(sys) +length(unknowns(sys)) ``` To solve the system, we first create an Stochastic Differential Equation Problem and then solve it over the tspan of (0,6e) using a EulerHeun solver. The solution is saved every 0.5ms. The unit of time in Neuroblox is 1ms. ```@example resting-state-circuit prob = SDEProblem(sys,rand(-2:0.1:4,76*2), (0.0, 6e5), []) -sol = solve(prob, EulerHeun(), dt=0.5) +sol = solve(prob, EulerHeun(), dt=0.5, saveat=5) plot(sol.t,sol[5,:],xlims=(0,10000)) ``` To evaluate the connectivity of our simulated resting state network, we calculate the statistically significant correlations @@ -72,7 +73,10 @@ for i in 1:76 p[i,j] = pvalue(OneSampleTTest(css[i,j,:])) end end - -plot(heatmap(wm,aspect_ratio = :equal), heatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal)) +heatmap(log10.(p) .* (p .< 0.05),aspect_ratio = :equal) +``` +Fig.: log10(p value) displaying statistally significant correlation between time series +```@example resting-state-circuit +heatmap(wm,aspect_ratio = :equal) ``` -Fig. Left: Connection Adjacency Matrix, Right: log10(p value) displaying statistally significant correlation between time series +Fig.: Connection Adjacency Matrix that was used to connect the neural mass models From 3f2f313993b035659c3f0a6cbc350612df8d8b23 Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Tue, 4 Jun 2024 18:47:31 -0400 Subject: [PATCH 20/25] example modification --- examples/Adab_DBS_example.jl | 44 ++++++++++++++++++++++++++++++++---- 1 file changed, 39 insertions(+), 5 deletions(-) diff --git a/examples/Adab_DBS_example.jl b/examples/Adab_DBS_example.jl index 784856b2..22543ed2 100644 --- a/examples/Adab_DBS_example.jl +++ b/examples/Adab_DBS_example.jl @@ -3,6 +3,8 @@ using DifferentialEquations using Graphs using MetaGraphs using Plots +using Statistics +using DSP global_ns = :g @named msn = Striatum_MSN_Adam(namespace=global_ns) @@ -14,13 +16,45 @@ assembly = [msn, fsi, gpe, stn] g = MetaDiGraph() add_blox!.(Ref(g), assembly) -add_edge!(g, 1, 3, Dict(:weight=> 2.5/100, :density=>0.33)) -add_edge!(g, 2, 1, Dict(:weight=> 0.6/50, :density=>0.15)) -add_edge!(g, 3, 4, Dict(:weight=> 0.3/80, :density=>0.05)) -add_edge!(g, 4, 2, Dict(:weight=> 0.165/40, :density=>0.1)) +add_edge!(g, 1, 3, Dict(:weight=> 2.5/33, :density=>0.33)) +add_edge!(g, 2, 1, Dict(:weight=> 0.6/7.5, :density=>0.15)) +add_edge!(g, 3, 4, Dict(:weight=> 0.3/4, :density=>0.05)) +add_edge!(g, 4, 2, Dict(:weight=> 0.165/4, :density=>0.1)) @named neuron_net = system_from_graph(g) sys = structural_simplify(neuron_net) prob = SDEProblem(sys, [], (0.0, 500), []) sol = solve(prob, saveat = 0.01) -ss=convert(Array,sol) \ No newline at end of file +ss=convert(Array,sol) + +st=unknowns(sys) +vlist=Int64[] +for ii = 1:length(st) + if contains(string(st[ii]), "V(t)") + push!(vlist,ii) + end +end +V = ss[vlist,:] + +VV=zeros(length(vlist),length(sol.t)) +for ii = 1:length(vlist) + VV[ii,:] .= V[ii,:] .+ 200*(ii-1) + +end + +mmsn=mean(V[1:100,:],dims=1) +mfsi=mean(V[101:150,:],dims=1) +mgpe=mean(V[151:230,:],dims=1) +mstn=mean(V[231:end,:],dims=1) + +V_av=mmsn + +fs = 1000; +avec = [ii*100+1 for ii = 1:500] +V_av = mean(V,dims=1) +periodogram_estimation = periodogram(V_av[1,avec], fs=fs) +#periodogram_estimation = welch_pgram(average1[1,avec], fs=fs) +pxx = periodogram_estimation.power +f = periodogram_estimation.freq + +plot!(f,log10.(pxx),xlabel="frequency Hz",ylabel="log(psd)",xlims=(0,150)) \ No newline at end of file From 22e48f447fcd6e3cd8f0d934f168b5c38f0f343a Mon Sep 17 00:00:00 2001 From: anandpathak31 Date: Tue, 4 Jun 2024 18:48:12 -0400 Subject: [PATCH 21/25] remove plots --- Project.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/Project.toml b/Project.toml index 35acaae8..29cc312a 100644 --- a/Project.toml +++ b/Project.toml @@ -40,7 +40,6 @@ OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1" OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed" Peaks = "18e31ff7-3703-566c-8e60-38913d67486b" -Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" Reexport = "189a3867-3050-52da-a836-e630ba90ab69" From 8990f932c08243ad5aa7fc71e66c005989b7c48f Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 12 Apr 2024 22:00:07 +0200 Subject: [PATCH 22/25] WIP: it runs through now with the newly defined ExternalInput block but the results are wrong. --- src/Neuroblox.jl | 2 +- src/blox/connections.jl | 24 ++++++++++++++++++++++++ src/blox/neural_mass.jl | 12 ++++-------- src/blox/sources.jl | 15 +++++++++++++++ test/datafitting.jl | 13 ++++++++----- 5 files changed, 52 insertions(+), 14 deletions(-) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 5a271df4..2a1a37d7 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -194,7 +194,7 @@ export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc export HebbianPlasticity, HebbianModulationPlasticity export Agent, ClassificationEnvironment, GreedyPolicy, reset! export LearningBlox -export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus +export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus, ExternalInput export PowerSpectrumBlox, BandPassFilterBlox export OUBlox, OUCouplingBlox export phase_inter, phase_sin_blox, phase_cos_blox diff --git a/src/blox/connections.jl b/src/blox/connections.jl index 27eaf62c..c4450e3e 100644 --- a/src/blox/connections.jl +++ b/src/blox/connections.jl @@ -418,6 +418,30 @@ function (bc::BloxConnector)( accumulate_equation!(bc, eq) end +# additional dispatch to connect to a stimulus blox, first crafted for ExternalInput +function (bc::BloxConnector)( + bloxout::StimulusBlox, + bloxin::NeuralMassBlox; + weight=1 +) + + sys_out = get_namespaced_sys(bloxout) + sys_in = get_namespaced_sys(bloxin) + + w_name = Symbol("w_$(nameof(sys_out))_$(nameof(sys_in))") + if typeof(weight) == Num # Symbol + w = weight + else + w = only(@parameters $(w_name)=weight) + end + push!(bc.weights, w) + + x = namespace_expr(bloxout.output, sys_out, nameof(sys_out)) + eq = sys_in.jcn ~ x*w + + accumulate_equation!(bc, eq) +end + # # Ok yes this is a bad dispatch but the whole compound blocks implementation is hacky and needs fixing @@ # # Opening an issue to loop back to this during clean up week # function (bc::BloxConnector)( diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl index b8621854..ea0826ae 100644 --- a/src/blox/neural_mass.jl +++ b/src/blox/neural_mass.jl @@ -99,19 +99,15 @@ Arguments: """ struct LinearNeuralMass <: NeuralMassBlox - params output jcn odesystem namespace - function LinearNeuralMass(;name, namespace=nothing, C=0.0625) - p = paramscoping(C=C) - C = setmetadata(p[1], VariableTunable, false) # TODO: change paramscoping to be able to pass tunable flag - sts = @variables x(t)=0.0 [output=true] jcn(t)=0.0 [input=true] u(t) [irreducible=true, description="ext_input"] - eqs = [D(x) ~ jcn + C*u, - u ~ 1.0] + function LinearNeuralMass(;name, namespace=nothing) + sts = @variables x(t)=0.0 [output=true] jcn(t)=0.0 [input=true] + eqs = [D(x) ~ jcn] sys = System(eqs, t, name=name) - new(p, sts[1], sts[2], sys, namespace) + new(sts[1], sts[2], sys, namespace) end end diff --git a/src/blox/sources.jl b/src/blox/sources.jl index 7f5dbb6c..fa0b74cd 100644 --- a/src/blox/sources.jl +++ b/src/blox/sources.jl @@ -1,3 +1,18 @@ +@parameters t + +# Simple input blox +mutable struct ExternalInput <: StimulusBlox + namespace + output::Num + odesystem::ODESystem + function ExternalInput(;name, I=1.0, namespace=nothing) + sts = @variables u(t) [irreducible=true, description="ext_input"] + eqs = [u ~ I] + odesys = System(eqs, t, sts, []; name=name) + new(namespace, sts[1], odesys) + end +end + #CosineSource mutable struct CosineSource f::Num diff --git a/test/datafitting.jl b/test/datafitting.jl index e3a9d5df..0e9628a3 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -3,24 +3,27 @@ using MAT ### Load data ### vars = matread(joinpath(@__DIR__, "spectralDCM_toydata.mat")); -data = DataFrame(vars["data"], :auto) # turn data into DataFrame -x = vars["x"] # initial conditions +data = DataFrame(vars["data"], :auto) # turn data into DataFrame +x = vars["x"] # initial conditions nrr = ncol(data) # number of recorded regions max_iter = 128 ########## assemble the model ########## g = MetaDiGraph() regions = Dict() -@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] # define brain-wide decay parameter for hemodynamics +@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=0.0625 [tunable = false] for ii = 1:nrr region = LinearNeuralMass(;name=Symbol("r$(ii)₊lm")) add_blox!(g, region) - regions[ii] = 2ii - 1 # store index of neural mass model + regions[ii] = nv(g) # store index of neural mass model + taskinput = ExternalInput(;name=Symbol("r$(ii)₊ei"), I=1.0) + add_blox!(g, taskinput) + add_edge!(g, nv(g), nv(g) - 1, Dict(:weight => C)) # add hemodynamic observer observer = BalloonModel(;name=Symbol("r$(ii)₊bm"), lnκ=lnκ, lnϵ=lnϵ) add_blox!(g, observer) # connect observer with neuronal signal - add_edge!(g, 2ii - 1, 2ii, Dict(:weight => 1.0)) + add_edge!(g, nv(g) - 2, nv(g), Dict(:weight => 1.0)) end # add symbolic weights From 91deb284ab8be681e9e150f997cdf69b9926ccc6 Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 12 Apr 2024 22:53:10 +0200 Subject: [PATCH 23/25] WIP: fix misalignment between states and jacobian (i.e. equations) of model --- test/datafitting.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/datafitting.jl b/test/datafitting.jl index 0e9628a3..1b85e923 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -11,7 +11,7 @@ max_iter = 128 g = MetaDiGraph() regions = Dict() -@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=0.0625 [tunable = false] +@parameters lnκ=0.0 [tunable = true] lnϵ=0.0 [tunable=true] C=1/16 [tunable = false] for ii = 1:nrr region = LinearNeuralMass(;name=Symbol("r$(ii)₊lm")) add_blox!(g, region) @@ -77,7 +77,7 @@ params_idx[:bold] = idx_bold # define prior variances paramvariance = copy(modelparam) paramvariance[:lnγ] = ones(Float64, nrr)./64.0; -paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0; +paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0; paramvariance[:lnβ] = ones(Float64, length(modelparam[:lnβ]))./64.0; for (k, v) in paramvariance if occursin("A[", string(k)) @@ -99,7 +99,7 @@ hyperpriors = (Πλ_pr = vars["ihC"]*ones(1, 1), # prior metaparameter precisi csdsetup = (p = 8, freq = vec(vars["Hz"]), dt = vars["dt"]); (state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, params_idx); -for iter in 1:128 +for iter in 1:max_iter state.iter = iter run_sDCM_iteration!(state, setup) print("iteration: ", iter, " - F:", state.F[end] - state.F[2], " - dF predicted:", state.dF[end], "\n") From 9509e34f26cf3921d0c691d8e931b62e9fdca18c Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Thu, 18 Apr 2024 00:25:22 +0200 Subject: [PATCH 24/25] problem fixed in a somewhat hacky way. Maybe revisit with MTK9 in place --- src/Neuroblox.jl | 2 +- src/blox/blox_utilities.jl | 46 +++++++++++++++++++++++++++------- src/datafitting/spectralDCM.jl | 35 +++++++++++++------------- test/datafitting.jl | 27 ++++++++++---------- 4 files changed, 69 insertions(+), 41 deletions(-) diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl index 2a1a37d7..09c5247e 100644 --- a/src/Neuroblox.jl +++ b/src/Neuroblox.jl @@ -209,7 +209,7 @@ export create_adjacency_edges!, adjmatrixfromdigraph export get_namespaced_sys, nameof export run_experiment!, run_trial! export addnontunableparams -export get_weights, get_dynamic_states +export get_weights, get_dynamic_states, get_idx_tagged_vars, get_eqidx_tagged_vars export BalloonModel, boldsignal_endo_balloon end diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl index fa187c29..0f37e505 100644 --- a/src/blox/blox_utilities.jl +++ b/src/blox/blox_utilities.jl @@ -263,18 +263,46 @@ end """ function get_dynamic_states(sys) sts = [] - idx_u = Int[] - idx_m = Int[] - for (i, s) in enumerate(unknowns(sys)) - if getdescription(s) == "ext_input" - push!(idx_u, i) - elseif getdescription(s) == "measurement" - push!(idx_m, i) - else + idx = [] + for (i, s) in enumerate(states(sys)) + if !((getdescription(s) == "ext_input") || (getdescription(s) == "measurement")) push!(sts, s) + push!(idx, i) + end + end + return sts, idx +end + +function get_eqidx_tagged_vars(sys, tag) + idx = Int[] + vars = [] + eqs = equations(sys) + for s in states(sys) + if getdescription(s) == tag + push!(vars, s) + end + end + + for v in vars + for (i, e) in enumerate(eqs) + for s in Symbolics.get_variables(e) + if string(s) == string(v) + push!(idx, i) + end + end + end + end + return idx +end + +function get_idx_tagged_vars(sys, tag) + idx = Int[] + for (i, s) in enumerate(states(sys)) + if (getdescription(s) == tag) + push!(idx, i) end end - sts, idx_u, idx_m + return idx end """ diff --git a/src/datafitting/spectralDCM.jl b/src/datafitting/spectralDCM.jl index 5b34c45a..2263419a 100644 --- a/src/datafitting/spectralDCM.jl +++ b/src/datafitting/spectralDCM.jl @@ -104,12 +104,11 @@ function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np} return Eigen(evals, evecs) end -function transferfunction_fmri(ω, derivatives, params, params_idx) - ∂f = derivatives(params[params_idx[:dspars]]) - idx_ds = deleteat!([1:size(∂f, 1);], sort(vcat(params_idx[:bold], params_idx[:u]))) - ∂f∂x = ∂f[idx_ds, idx_ds] - ∂f∂u = ∂f[idx_ds, params_idx[:u]] - ∂g∂x = ∂f[params_idx[:bold], idx_ds] +function transferfunction_fmri(ω, derivatives, params, indices) + ∂f = derivatives(params[indices[:dspars]]) + ∂f∂x = ∂f[indices[:sts], indices[:sts]] + ∂f∂u = ∂f[indices[:sts], indices[:u]] + ∂g∂x = ∂f[indices[:bold], indices[:sts]] F = eigen(∂f∂x) Λ = F.values @@ -144,14 +143,14 @@ end Gn in the code corresponds to Ge in the paper, i.e. the observation noise. In the code global and local components are defined, no such distinction is discussed in the paper. In fact the parameter γ, corresponding to local component is not present in the paper. """ -function csd_approx(ω, derivatives, params, params_idx) +function csd_approx(ω, derivatives, params, indices) # priors of spectral parameters # ln(α) and ln(β), region specific fluctuations: ln(γ) nω = length(ω) - nd = length(params_idx[:lnγ]) - α = params[params_idx[:lnα]] - β = params[params_idx[:lnβ]] - γ = params[params_idx[:lnγ]] + nd = length(indices[:lnγ]) + α = params[indices[:lnα]] + β = params[indices[:lnβ]] + γ = params[indices[:lnγ]] # define function that implements spectra given in equation (2) of the paper "A DCM for resting state fMRI". @@ -177,7 +176,7 @@ function csd_approx(ω, derivatives, params, params_idx) Gn[:,j,i] = Gn[:,i,j] end end - S = transferfunction_fmri(ω, derivatives, params, params_idx) # This is K(ω) in the equations of the spectral DCM paper. + S = transferfunction_fmri(ω, derivatives, params, indices) # This is K(ω) in the equations of the spectral DCM paper. # predicted cross-spectral density G = zeros(eltype(S), nω, nd, nd); @@ -188,8 +187,8 @@ function csd_approx(ω, derivatives, params, params_idx) return G + Gn end -@views function csd_fmri_mtf(freqs, p, derivatives, params, params_idx) # alongside the above realtes to spm_csd_fmri_mtf.m - G = csd_approx(freqs, derivatives, params, params_idx) +@views function csd_fmri_mtf(freqs, p, derivatives, params, indices) # alongside the above realtes to spm_csd_fmri_mtf.m + G = csd_approx(freqs, derivatives, params, indices) dt = 1/(2*freqs[end]) # the following two steps are very opaque. They are taken from the SPM code but it is unclear what the purpose of this transformation and back-transformation is # in particular it is also unclear why the order of the MAR is reduced by 1. My best guess is that this procedure smoothens the results. @@ -283,7 +282,7 @@ end """ - function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, params_idx) + function setup_sDCM(data, stateevolutionmodel, initcond, csdsetup, priors, hyperpriors, indices) Interface function to performs variational inference to fit model parameters to empirical cross spectral density. The current implementation provides a Variational Laplace fit (see function above `variationalbayes`). @@ -303,9 +302,9 @@ end - `hyperpriors` : dataframe of parameters with the following columns: -- `Πλ_pr` : prior precision matrix for λ hyperparameter(s) -- `μλ_pr` : prior mean(s) for λ hyperparameter(s) - - `params_idx` : indices to separate model parameters from other parameters. Needed for the computation of AD gradient. + - `indices` : indices to separate model parameters from other parameters. Needed for the computation of AD gradient. """ -function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params_idx) +function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, indices) # compute cross-spectral density dt = csdsetup[:dt]; # order of MAR. Hard-coded in SPM12 with this value. We will use the same for now. ω = csdsetup[:freq]; # frequencies at which the CSD is evaluated @@ -325,7 +324,7 @@ function setup_sDCM(data, model, initcond, csdsetup, priors, hyperpriors, params nq = 1 # TODO: this is hard-coded, need to make this compliant with csd_Q nh = size(Q, 3) # number of precision components (this is the same as above, but may differ) - f = params -> csd_fmri_mtf(ω, p, derivatives, params, params_idx) + f = params -> csd_fmri_mtf(ω, p, derivatives, params, indices) np = length(μθ_pr) # number of parameters ny = length(y_csd) # total number of response variables diff --git a/test/datafitting.jl b/test/datafitting.jl index 1b85e923..96df7e39 100644 --- a/test/datafitting.jl +++ b/test/datafitting.jl @@ -41,14 +41,15 @@ end neuronmodel = structural_simplify(neuronmodel; split=false) # attribute initial conditions to states -ds_states, idx_u, idx_bold = get_dynamic_states(neuronmodel) -initcond = OrderedDict(ds_states .=> 0.0) - +sts, idx_sts = get_dynamic_states(neuronmodel) +idx_u = get_idx_tagged_vars(neuronmodel, "ext_input") # get index of external input state +idx_bold = get_eqidx_tagged_vars(neuronmodel, "measurement") # get index of equation of bold state +initcond = OrderedDict(sts .=> 0.0) rnames = [] -map(x->push!(rnames, split(string(x), "₊")[1]), ds_states); +map(x->push!(rnames, split(string(x), "₊")[1]), sts); rnames = unique(rnames); for (i, r) in enumerate(rnames) - for (j, s) in enumerate(ds_states[r .== map(x -> x[1], split.(string.(ds_states), "₊"))]) + for (j, s) in enumerate(sts[r .== map(x -> x[1], split.(string.(sts), "₊"))]) initcond[s] = x[i, j] end end @@ -58,22 +59,22 @@ for par in tunable_parameters(neuronmodel) modelparam[par] = Symbolics.getdefaultval(par) end np = length(modelparam) -params_idx = Dict(:dspars => collect(1:np)) +indices = Dict(:dspars => collect(1:np)) # Noise parameter mean modelparam[:lnα] = [0.0, 0.0]; # intrinsic fluctuations, ln(α) as in equation 2 of Friston et al. 2014 n = length(modelparam[:lnα]); -params_idx[:lnα] = collect(np+1:np+n); +indices[:lnα] = collect(np+1:np+n); np += n; modelparam[:lnβ] = [0.0, 0.0]; # global observation noise, ln(β) as above n = length(modelparam[:lnβ]); -params_idx[:lnβ] = collect(np+1:np+n); +indices[:lnβ] = collect(np+1:np+n); np += n; modelparam[:lnγ] = zeros(Float64, nrr); # region specific observation noise -params_idx[:lnγ] = collect(np+1:np+nrr); +indices[:lnγ] = collect(np+1:np+nrr); np += nrr -params_idx[:u] = idx_u -params_idx[:bold] = idx_bold - +indices[:u] = idx_u +indices[:bold] = idx_bold +indices[:sts] = idx_sts # define prior variances paramvariance = copy(modelparam) paramvariance[:lnγ] = ones(Float64, nrr)./64.0; @@ -98,7 +99,7 @@ hyperpriors = (Πλ_pr = vars["ihC"]*ones(1, 1), # prior metaparameter precisi csdsetup = (p = 8, freq = vec(vars["Hz"]), dt = vars["dt"]); -(state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, params_idx); +(state, setup) = setup_sDCM(data, neuronmodel, initcond, csdsetup, priors, hyperpriors, indices); for iter in 1:max_iter state.iter = iter run_sDCM_iteration!(state, setup) From 1bc0cd6b1942fd268440d6910f7d84cf475a2236 Mon Sep 17 00:00:00 2001 From: david-hofmann Date: Fri, 7 Jun 2024 17:08:48 +0200 Subject: [PATCH 25/25] merged with MTK9 version of Neuroblox --- src/blox/blox_utilities.jl | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl index 0f37e505..6b450e92 100644 --- a/src/blox/blox_utilities.jl +++ b/src/blox/blox_utilities.jl @@ -264,7 +264,7 @@ end function get_dynamic_states(sys) sts = [] idx = [] - for (i, s) in enumerate(states(sys)) + for (i, s) in enumerate(unknowns(sys)) if !((getdescription(s) == "ext_input") || (getdescription(s) == "measurement")) push!(sts, s) push!(idx, i) @@ -277,7 +277,7 @@ function get_eqidx_tagged_vars(sys, tag) idx = Int[] vars = [] eqs = equations(sys) - for s in states(sys) + for s in unknowns(sys) if getdescription(s) == tag push!(vars, s) end @@ -297,7 +297,7 @@ end function get_idx_tagged_vars(sys, tag) idx = Int[] - for (i, s) in enumerate(states(sys)) + for (i, s) in enumerate(unknowns(sys)) if (getdescription(s) == tag) push!(idx, i) end

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