Deploying to gh-pages from @ 62e4527 🚀

assets/pages/blox_connections/code/output/ex6.res

@@ -1,4 +1,4 @@
-Main.FD_SANDBOX_15604780526313532720.IzhNeuron(ModelingToolkit.ODESystem(0x0000000000001fd1, Symbolics.Equation[Differential(t)(V(t)) ~ 145 + jcn - u(t) + 5V(t) + 0.04(V(t)^2), Differential(t)(u(t)) ~ a*(-u(t) + b*V(t))], t, SymbolicUtils.BasicSymbolic{Real}[V(t), u(t), jcn], SymbolicUtils.BasicSymbolic{Real}[a, b, V_reset, d, θ], nothing, Dict{Any, Any}(:a => a, :b => b, :d => d, :V => V(t), :jcn => jcn, :u => u(t), :θ => θ, :V_reset => V_reset), Any[], Symbolics.Equation[], Base.RefValue{Vector{Symbolics.Num}}(Symbolics.Num[]), Base.RefValue{Any}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Matrix{Symbolics.Num}}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Matrix{Symbolics.Num}}(Matrix{Symbolics.Num}(undef, 0, 0)), :izh, "", ModelingToolkit.ODESystem[], Dict{Any, Any}(a => 0.02, V_reset => -50, d => 2, u(t) => -13, V(t) => -65.0, b => 0.2, θ => 30), Dict{Any, Any}(), nothing, nothing, Symbolics.Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[condition: V(t) > θ
+Main.FD_SANDBOX_15604780526313532720.IzhNeuron(ModelingToolkit.ODESystem(0x0000000000002011, Symbolics.Equation[Differential(t)(V(t)) ~ 145 + jcn - u(t) + 5V(t) + 0.04(V(t)^2), Differential(t)(u(t)) ~ a*(-u(t) + b*V(t))], t, SymbolicUtils.BasicSymbolic{Real}[V(t), u(t), jcn], SymbolicUtils.BasicSymbolic{Real}[a, b, V_reset, d, θ], nothing, Dict{Any, Any}(:a => a, :b => b, :d => d, :V => V(t), :jcn => jcn, :u => u(t), :θ => θ, :V_reset => V_reset), Any[], Symbolics.Equation[], Base.RefValue{Vector{Symbolics.Num}}(Symbolics.Num[]), Base.RefValue{Any}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Any}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Matrix{Symbolics.Num}}(Matrix{Symbolics.Num}(undef, 0, 0)), Base.RefValue{Matrix{Symbolics.Num}}(Matrix{Symbolics.Num}(undef, 0, 0)), :izh, "", ModelingToolkit.ODESystem[], Dict{Any, Any}(a => 0.02, V_reset => -50, d => 2, u(t) => -13, V(t) => -65.0, b => 0.2, θ => 30), Dict{Any, Any}(), nothing, nothing, Symbolics.Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[condition: V(t) > θ
 affects:
   u(t) ~ d + u(t)
   V(t) ~ V_reset

assets/pages/learning/code/output/ex3.out

@@ -1,10 +1,10 @@
-Trial = 1, Category choice = 1, Response = False
-Trial = 2, Category choice = 1, Response = Correct
-Trial = 3, Category choice = 2, Response = False
-Trial = 4, Category choice = 2, Response = Correct
+Trial = 1, Category choice = 2, Response = Correct
+Trial = 2, Category choice = 2, Response = False
+Trial = 3, Category choice = 1, Response = Correct
+Trial = 4, Category choice = 1, Response = False
 Trial = 5, Category choice = 2, Response = False
 Trial = 6, Category choice = 1, Response = False
-Trial = 7, Category choice = 2, Response = Correct
+Trial = 7, Category choice = 1, Response = False
 Trial = 8, Category choice = 1, Response = False
 Trial = 9, Category choice = 2, Response = Correct
 Trial = 10, Category choice = 2, Response = Correct
@@ -15,86 +15,86 @@ Trial = 14, Category choice = 2, Response = Correct
 Trial = 15, Category choice = 1, Response = Correct
 Trial = 16, Category choice = 2, Response = False
 Trial = 17, Category choice = 1, Response = Correct
-Trial = 18, Category choice = 1, Response = Correct
+Trial = 18, Category choice = 2, Response = False
 Trial = 19, Category choice = 2, Response = False
-Trial = 20, Category choice = 2, Response = Correct
-Trial = 21, Category choice = 2, Response = False
+Trial = 20, Category choice = 1, Response = False
+Trial = 21, Category choice = 1, Response = Correct
 Trial = 22, Category choice = 1, Response = Correct
 Trial = 23, Category choice = 2, Response = False
 Trial = 24, Category choice = 2, Response = Correct
-Trial = 25, Category choice = 2, Response = False
-Trial = 26, Category choice = 2, Response = False
-Trial = 27, Category choice = 1, Response = Correct
-Trial = 28, Category choice = 2, Response = False
+Trial = 25, Category choice = 1, Response = Correct
+Trial = 26, Category choice = 1, Response = Correct
+Trial = 27, Category choice = 2, Response = False
+Trial = 28, Category choice = 1, Response = Correct
 Trial = 29, Category choice = 2, Response = Correct
 Trial = 30, Category choice = 2, Response = False
-Trial = 31, Category choice = 2, Response = Correct
+Trial = 31, Category choice = 1, Response = False
 Trial = 32, Category choice = 1, Response = Correct
-Trial = 33, Category choice = 1, Response = False
+Trial = 33, Category choice = 2, Response = Correct
 Trial = 34, Category choice = 2, Response = Correct
-Trial = 35, Category choice = 2, Response = Correct
+Trial = 35, Category choice = 1, Response = False
 Trial = 36, Category choice = 2, Response = False
-Trial = 37, Category choice = 1, Response = Correct
+Trial = 37, Category choice = 2, Response = False
 Trial = 38, Category choice = 2, Response = False
 Trial = 39, Category choice = 2, Response = Correct
 Trial = 40, Category choice = 2, Response = False
 Trial = 41, Category choice = 2, Response = Correct
 Trial = 42, Category choice = 1, Response = Correct
 Trial = 43, Category choice = 2, Response = False
-Trial = 44, Category choice = 2, Response = Correct
-Trial = 45, Category choice = 1, Response = Correct
-Trial = 46, Category choice = 1, Response = Correct
+Trial = 44, Category choice = 1, Response = False
+Trial = 45, Category choice = 2, Response = False
+Trial = 46, Category choice = 2, Response = False
 Trial = 47, Category choice = 2, Response = False
-Trial = 48, Category choice = 1, Response = False
-Trial = 49, Category choice = 2, Response = Correct
+Trial = 48, Category choice = 2, Response = Correct
+Trial = 49, Category choice = 1, Response = False
 Trial = 50, Category choice = 1, Response = False
-Trial = 51, Category choice = 1, Response = Correct
-Trial = 52, Category choice = 2, Response = False
+Trial = 51, Category choice = 2, Response = False
+Trial = 52, Category choice = 1, Response = Correct
 Trial = 53, Category choice = 1, Response = False
-Trial = 54, Category choice = 2, Response = Correct
+Trial = 54, Category choice = 1, Response = False
 Trial = 55, Category choice = 2, Response = Correct
-Trial = 56, Category choice = 1, Response = False
+Trial = 56, Category choice = 2, Response = Correct
 Trial = 57, Category choice = 2, Response = False
-Trial = 58, Category choice = 2, Response = False
-Trial = 59, Category choice = 1, Response = Correct
+Trial = 58, Category choice = 1, Response = Correct
+Trial = 59, Category choice = 2, Response = False
 Trial = 60, Category choice = 1, Response = Correct
 Trial = 61, Category choice = 1, Response = False
 Trial = 62, Category choice = 2, Response = Correct
-Trial = 63, Category choice = 2, Response = Correct
+Trial = 63, Category choice = 1, Response = False
 Trial = 64, Category choice = 2, Response = False
 Trial = 65, Category choice = 2, Response = False
-Trial = 66, Category choice = 1, Response = False
+Trial = 66, Category choice = 2, Response = Correct
 Trial = 67, Category choice = 2, Response = False
-Trial = 68, Category choice = 2, Response = Correct
+Trial = 68, Category choice = 1, Response = False
 Trial = 69, Category choice = 1, Response = False
-Trial = 70, Category choice = 2, Response = False
+Trial = 70, Category choice = 1, Response = Correct
 Trial = 71, Category choice = 1, Response = Correct
-Trial = 72, Category choice = 1, Response = False
-Trial = 73, Category choice = 1, Response = False
-Trial = 74, Category choice = 1, Response = Correct
-Trial = 75, Category choice = 1, Response = Correct
-Trial = 76, Category choice = 2, Response = Correct
+Trial = 72, Category choice = 2, Response = Correct
+Trial = 73, Category choice = 2, Response = Correct
+Trial = 74, Category choice = 2, Response = False
+Trial = 75, Category choice = 2, Response = False
+Trial = 76, Category choice = 1, Response = False
 Trial = 77, Category choice = 2, Response = False
-Trial = 78, Category choice = 2, Response = False
+Trial = 78, Category choice = 1, Response = Correct
 Trial = 79, Category choice = 1, Response = Correct
-Trial = 80, Category choice = 1, Response = Correct
+Trial = 80, Category choice = 2, Response = False
 Trial = 81, Category choice = 1, Response = False
 Trial = 82, Category choice = 1, Response = Correct
 Trial = 83, Category choice = 2, Response = False
 Trial = 84, Category choice = 2, Response = Correct
-Trial = 85, Category choice = 2, Response = Correct
-Trial = 86, Category choice = 2, Response = False
+Trial = 85, Category choice = 1, Response = False
+Trial = 86, Category choice = 1, Response = Correct
 Trial = 87, Category choice = 1, Response = Correct
-Trial = 88, Category choice = 1, Response = Correct
+Trial = 88, Category choice = 2, Response = False
 Trial = 89, Category choice = 2, Response = Correct
-Trial = 90, Category choice = 1, Response = Correct
-Trial = 91, Category choice = 1, Response = False
+Trial = 90, Category choice = 2, Response = False
+Trial = 91, Category choice = 2, Response = Correct
 Trial = 92, Category choice = 2, Response = False
-Trial = 93, Category choice = 1, Response = False
+Trial = 93, Category choice = 2, Response = Correct
 Trial = 94, Category choice = 2, Response = Correct
-Trial = 95, Category choice = 1, Response = Correct
+Trial = 95, Category choice = 2, Response = False
 Trial = 96, Category choice = 2, Response = Correct
-Trial = 97, Category choice = 1, Response = False
-Trial = 98, Category choice = 2, Response = Correct
-Trial = 99, Category choice = 1, Response = Correct
-Trial = 100, Category choice = 1, Response = Correct
+Trial = 97, Category choice = 2, Response = Correct
+Trial = 98, Category choice = 1, Response = False
+Trial = 99, Category choice = 2, Response = False
+Trial = 100, Category choice = 2, Response = False

assets/pages/learning/code/output/ex3.res

@@ -1 +1 @@
-(trial = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], correct = Bool[0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1], action = [1, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1])
+(trial = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100], correct = Bool[1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0], action = [2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2])

notebooks/CS_circuit.ipynb

@@ -106,13 +106,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{wta_stack}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -179,13 +172,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{wta_wta_stack}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -311,13 +297,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{cort_stack}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -399,13 +378,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{cort_asc_stack}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -423,13 +395,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{cort_meanfield}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -446,13 +411,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{cort_power}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -577,13 +535,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{image_stim}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [

notebooks/DCM.ipynb

@@ -114,40 +114,12 @@
    "source": [
     "for i = 1:nr\n",
     "    region = LinearNeuralMass(;name=Symbol(\"r$(i)₊lm\"))\n",
-    "    push!(regions, region)          # store neural mass model in list. We need this list below. If you haven't seen the Julia command `push!` before [see here](http://jlhub.com/julia/manual/en/function/push-exclamation)."
-   ],
-   "metadata": {},
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "add Ornstein-Uhlenbeck block as noisy input to the current region"
-   ],
-   "metadata": {}
-  },
-  {
-   "outputs": [],
-   "cell_type": "code",
-   "source": [
-    "    input = OUBlox(;name=Symbol(\"r$(i)₊ou\"), σ=0.1)\n",
-    "    add_edge!(g, input => region, weight=1/16)   # Note that 1/16 is taken from SPM12, this stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability."
-   ],
-   "metadata": {},
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "simulate fMRI signal with BalloonModel which includes the BOLD signal on top of the balloon model dynamics"
-   ],
-   "metadata": {}
-  },
-  {
-   "outputs": [],
-   "cell_type": "code",
-   "source": [
-    "    measurement = BalloonModel(;name=Symbol(\"r$(i)₊bm\"))\n",
+    "    push!(regions, region)          # store neural mass model in list. We need this list below. If you haven't seen the Julia command `push!` before [see here](http://jlhub.com/julia/manual/en/function/push-exclamation).\n",
+    "\n",
+    "    input = OUBlox(;name=Symbol(\"r$(i)₊ou\"), σ=0.1) ## add Ornstein-Uhlenbeck as noisy input to the current region\n",
+    "    add_edge!(g, input => region, weight=1/16)\n",
+    "\n",
+    "    measurement = BalloonModel(;name=Symbol(\"r$(i)₊bm\")) ## simulate fMRI signal with BalloonModel which includes the BOLD signal on top of the balloon model dynamics\n",
     "    add_edge!(g, region => measurement, weight=1.0)\n",
     "end"
    ],
@@ -157,6 +129,7 @@
   {
    "cell_type": "markdown",
    "source": [
+    "Note that `weight=1/16` in the connection between the OU process and the Neural Mass Blox is taken from SPM12. This stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability.\n",
     "Next we define the between-region connectivity matrix and connect regions; we use the same matrix as is defined in [3]"
    ],
    "metadata": {}
@@ -256,13 +229,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{fmriseries}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -339,13 +305,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{csd}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -408,23 +367,9 @@
     "    region = LinearNeuralMass(;name=Symbol(\"r$(i)₊lm\"))\n",
     "    push!(regions, region)\n",
     "    input = ExternalInput(;name=Symbol(\"r$(i)₊ei\"))\n",
-    "    add_edge!(g, input => region, weight=C)"
-   ],
-   "metadata": {},
-   "execution_count": null
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "we assume fMRI signal and model them with a BalloonModel"
-   ],
-   "metadata": {}
-  },
-  {
-   "outputs": [],
-   "cell_type": "code",
-   "source": [
-    "    measurement = BalloonModel(;name=Symbol(\"r$(i)₊bm\"), lnτ=lnτ, lnκ=lnκ, lnϵ=lnϵ)\n",
+    "    add_edge!(g, input => region, weight=C)\n",
+    "\n",
+    "    measurement = BalloonModel(;name=Symbol(\"r$(i)₊bm\"), lnτ=lnτ, lnκ=lnκ, lnϵ=lnϵ) ## assume fMRI signal and model them with a BalloonModel\n",
     "    add_edge!(g, region => measurement, weight=1.0)\n",
     "end"
    ],
@@ -712,13 +657,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{freeenergy}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -736,13 +674,6 @@
    "metadata": {},
    "execution_count": null
   },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\\fig{ecbar}"
-   ],
-   "metadata": {}
-  },
   {
    "cell_type": "markdown",
    "source": [