diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 707107c..9d66c03 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-02-09T11:36:50","documenter_version":"1.2.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-02-09T14:40:41","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index 6f1160e..4ec58b4 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -1,21 +1,21 @@ -API · Neuroblox
GitHub

API Documentation

Neuroblox.Agent — Type

function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem) ps = parameters(sys) params = prob.p mapidxs = Int.(ModelingToolkit.varmaptovars([ps[i] => i for i in eachindex(ps)], ps)) compparams = p.competitorparams idxscp = Int64[] for i in eachindex(compparams) idxs = findall(x -> x==compparams[i], ps) push!(idxscp,idxs) end compvals = params[mapidxs[idxscp]] @info compvals return argmax(compvals) end

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Neuroblox.BalloonModel — Type
where ``jcn`` is any input to the blox (represents the neuronal activity)

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.

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.HarmonicOscillator — Type
HarmonicOscillator(name, namespace, ω, ζ, k, h)
+API · Neuroblox
GitHub
API Documentation
Neuroblox.Agent — Type
function (p::GreedyPolicy)(sys::ODESystem, prob::ODEProblem)     ps = parameters(sys)     params = prob.p     mapidxs = Int.(ModelingToolkit.varmaptovars([ps[i] => i for i in eachindex(ps)], ps))     compparams = p.competitorparams     idxscp = Int64[]     for i in eachindex(compparams)         idxs = findall(x -> x==compparams[i], ps)         push!(idxscp,idxs)     end     compvals = params[mapidxs[idxscp]]     @info compvals     return argmax(compvals) end
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Neuroblox.BalloonModel — Type
where ``jcn`` is any input to the blox (represents the neuronal activity)
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.
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.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)
+\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.
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Neuroblox.JansenRit — Type
JansenRit(name, namespace, τ, H, λ, r, cortical)
 
 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.
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.
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Neuroblox.LIFNeuronBlox — Type
Standard Leaky Integrate and Fire neuron model.
variables:     V(t):  Membrane voltage     jcn:   Input from other neurons parameters:     Iin:   Input current 	VL:    Resting state potential     τ:      Membrane timescale 	R:      Membrane resistance 	θ:      Spike threshold 	st:     Last spike time 	strain: Spike train returns:     an ODE System
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Neuroblox.LarterBreakspear — Type
LarterBreakspear(name, namespace, ...)
+\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.
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.LIFNeuronBlox — Type
Standard Leaky Integrate and Fire neuron model.
variables:     V(t):  Membrane voltage     jcn:   Input from other neurons parameters:     Iin:   Input current 	VL:    Resting state potential     τ:      Membrane timescale 	R:      Membrane resistance 	θ:      Spike threshold 	st:     Last spike time 	strain: Spike train returns:     an ODE System
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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. 
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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)
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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
WilsonCown(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)
+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
WilsonCown(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}
+\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.
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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
@@ -25,7 +25,7 @@
 - `M`: matrix of type Dual of which to compute the eigenvalue decomposition. 
 
 Returns:
-- `Eigen(evals, evecs)`: eigenvalue decomposition returned as type LinearAlgebra.Eigen
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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]).
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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.
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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.
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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)
+- `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.
@@ -35,12 +35,12 @@
 - `sys`: MTK system
 
 Returns:
-- `completeparamlist`: complete parameter list of a system, including those that were not tagged as tunable
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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".
+- `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.
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Neuroblox.get_hemodynamic_observers — Method
function get_hemodynamic_observers(sys, nr)
+is discussed in the paper. In fact the parameter γ, corresponding to local component is not present in the paper.
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Neuroblox.get_hemodynamic_observers — Method
function get_hemodynamic_observers(sys, nr)
 
 Function extracts those states of an MTK system that were tagged "hemodynamic_observer".
 
@@ -50,9 +50,9 @@
 
 Returns:
 - `obs_idx`: indices of states with "hemodynamic_observer" tag in MTK system
-- `obs_states`: states with "hemodynamic_observer" tag in MTK system
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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.
+- `obs_states`: states with "hemodynamic_observer" tag in MTK system
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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, 
+that will later be composed and will automatically get the outermost namespace.
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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.
 
@@ -62,18 +62,18 @@
 
 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.
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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
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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
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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.spectralVI — Method
spectralVI(data, neuraldynmodel, observationmodel, initcond, csdsetup, params, hyperparams)
+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.spectralVI — Method
spectralVI(data, neuraldynmodel, observationmodel, initcond, csdsetup, params, hyperparams)
 
 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`).
@@ -94,12 +94,12 @@
 -- `variance`        : corresponds to the prior variances
 - `hyperparams`      : 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)
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.variationalbayes — Method
variationalbayes(idx_A, y, derivatives, w, V, p, priors, niter)
 
 Computes parameter estimation using variational Laplace that is to a large extend equivalent to the SPM12 implementation
 and provides the exact same values.
@@ -112,9 +112,9 @@
 - `V`: projection matrix from full parameter space to reduced space that removes parameters with zero variance prior
 - `p`: order of multivariate autoregressive model for estimation of cross-spectral densities from data
 - `priors`: Bayesian priors, mean and variance thereof. Laplace approximation assumes Gaussian distributions
-- `niter`: number of iterations of the optimization procedure
source
Neuroblox.vecparam — Method
vecparam(param::OrderedDict{Any, Any})
+- `niter`: number of iterations of the optimization procedure
source
Neuroblox.vecparam — Method
vecparam(param::OrderedDict{Any, Any})
 
 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

+- `param`: dictionary of model parameters (may contain numbers and lists of numbers)
source
diff --git a/dev/assets/Manifest.toml b/dev/assets/Manifest.toml index e4f9c05..2fad45e 100644 --- a/dev/assets/Manifest.toml +++ b/dev/assets/Manifest.toml @@ -2673,9 +2673,9 @@ version = "1.0.1" [[deps.Static]] deps = ["IfElse"] -git-tree-sha1 = "b366eb1eb68075745777d80861c6706c33f588ae" +git-tree-sha1 = "d2fdac9ff3906e27f7a618d47b676941baa6c80c" uuid = "aedffcd0-7271-4cad-89d0-dc628f76c6d3" -version = "0.8.9" +version = "0.8.10" [[deps.StaticArrayInterface]] deps = ["ArrayInterface", "Compat", "IfElse", "LinearAlgebra", "PrecompileTools", "Requires", "SparseArrays", "Static", "SuiteSparse"] diff --git a/dev/getting_started/f8874413.svg b/dev/getting_started/1d73ba25.svg similarity index 93% rename from dev/getting_started/f8874413.svg rename to dev/getting_started/1d73ba25.svg index f51bdb1..3511578 100644 --- a/dev/getting_started/f8874413.svg +++ b/dev/getting_started/1d73ba25.svg @@ -1,54 +1,54 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/getting_started/77e31729.svg b/dev/getting_started/35352fcf.svg similarity index 97% rename from dev/getting_started/77e31729.svg rename to dev/getting_started/35352fcf.svg index 1860b97..93e7b86 100644 --- a/dev/getting_started/77e31729.svg +++ b/dev/getting_started/35352fcf.svg @@ -1,78 +1,78 @@ - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/getting_started/index.html b/dev/getting_started/index.html index 8c712a8..848b21e 100644 --- a/dev/getting_started/index.html +++ b/dev/getting_started/index.html @@ -18,7 +18,7 @@ prob = ODEProblem(sys, [], (0.0, 100), []) sol = solve(prob, AutoVern7(Rodas4()), saveat=0.1) -plot(sol)Example block output

Example 2 : Building a Brain Circuit using Neural Mass Models

In this example, we will construct a Parkinsons model from eight Jansen-Rit Neural Mass Models. The Jansen-Rit Neural Mass model is defined by the following differential equations:

\[\frac{dx}{dt} = y-\frac{2}{\tau}x +plot(sol)Example block output

Example 2 : Building a Brain Circuit using Neural Mass Models

In this example, we will construct a Parkinsons model from eight Jansen-Rit Neural Mass Models. 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
@@ -62,4 +62,4 @@
 alg = MethodOfSteps(Vern7())
 sol_dde_no_delays = solve(prob, alg, saveat=1)
 
-plot(sol_dde_no_delays)
Example block output +plot(sol_dde_no_delays)Example block output diff --git a/dev/index.html b/dev/index.html index 5ef8638..a39f07c 100644 --- a/dev/index.html +++ b/dev/index.html @@ -4,4 +4,4 @@ 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.

+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.

diff --git a/dev/release_notes/index.html b/dev/release_notes/index.html index 3cd33c7..dbe6716 100644 --- a/dev/release_notes/index.html +++ b/dev/release_notes/index.html @@ -1,2 +1,2 @@ -Release Notes · Neuroblox
GitHub
+Release Notes · Neuroblox
GitHub