Deploying to gh-pages from @ 9e52c8f 🚀

Manifest.toml

@@ -1756,9 +1756,9 @@ version = "1.2.0"
 
 [[deps.Neuroblox]]
 deps = ["Accessors", "CSV", "ChainRules", "Colors", "Combinatorics", "DSP", "DataFrames", "Distributions", "ExponentialUtilities", "ForwardDiff", "GraphDynamics", "Graphs", "Interpolations", "LinearAlgebra", "LogExpFunctions", "MetaGraphs", "ModelingToolkit", "ModelingToolkitStandardLibrary", "MuladdMacro", "OhMyThreads", "OrderedCollections", "Peaks", "Random", "RecursiveArrayTools", "Reexport", "SciMLBase", "SparseArrays", "Statistics", "StatsBase", "StochasticDiffEq", "Symbolics", "ToeplitzMatrices"]
-git-tree-sha1 = "fe931b1c56c8160bd8e830d31d0ad1e1884b7f96"
+git-tree-sha1 = "a1728a560cd81b183bd35539739d50ef544f0150"
 uuid = "769b91e5-4c60-41ee-bfae-153c84203cb2"
-version = "0.5.4"
+version = "0.5.5"
 weakdeps = ["CairoMakie", "Makie"]
 
     [deps.Neuroblox.extensions]

assets/literate/learning.md

@@ -54,7 +54,7 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ##
 agent = Agent(g; name=model_name);
 env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name);
 
-fig = Figure(title="Adjacency matrix", size = (1600, 800))
+fig = Figure(size = (1600, 800))
 
 adjacency(fig[1,1], agent; title="Initial weights", colorrange=(0,10))
 

assets/literate/learning_script.jl

@@ -39,7 +39,7 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ##
 agent = Agent(g; name=model_name);
 env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name);
 
-fig = Figure(title="Adjacency matrix", size = (1600, 800))
+fig = Figure(size = (1600, 800))
 
 adjacency(fig[1,1], agent; title="Initial weights", colorrange=(0,10))
 

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

@@ -1,4 +1,4 @@
-Main.FD_SANDBOX_13439752999271061286.IzhNeuron(ModelingToolkit.ODESystem(0x0000000000001fed, 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, 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_13439752999271061286.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, 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/ex1.jl

@@ -38,7 +38,7 @@ add_edge!(g, VAC => AC, weight=3, density=0.1, learning_rule = hebbian_cort) ##
 agent = Agent(g; name=model_name);
 env = ClassificationEnvironment(stim, N_trials; name=:env, namespace=model_name);
 
-fig = Figure(title="Adjacency matrix", size = (1600, 800))
+fig = Figure(size = (1600, 800))
 
 adjacency(fig[1,1], agent; title="Initial weights", colorrange=(0,10))
 

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

@@ -1,78 +1,78 @@
-Trial = 1, Category choice = 2, Response = Correct
+Trial = 1, Category choice = 1, Response = False
 Trial = 2, Category choice = 2, Response = False
 Trial = 3, Category choice = 2, Response = False
-Trial = 4, Category choice = 2, Response = Correct
-Trial = 5, 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 = 1, Response = False
 Trial = 8, Category choice = 1, Response = False
 Trial = 9, Category choice = 1, Response = False
-Trial = 10, Category choice = 1, Response = False
-Trial = 11, Category choice = 2, Response = Correct
+Trial = 10, Category choice = 2, Response = Correct
+Trial = 11, Category choice = 1, Response = False
 Trial = 12, Category choice = 1, Response = False
-Trial = 13, Category choice = 1, Response = Correct
+Trial = 13, Category choice = 2, Response = False
 Trial = 14, Category choice = 1, Response = False
-Trial = 15, Category choice = 2, Response = False
-Trial = 16, Category choice = 2, Response = False
-Trial = 17, Category choice = 1, Response = Correct
+Trial = 15, Category choice = 1, Response = Correct
+Trial = 16, Category choice = 1, Response = Correct
+Trial = 17, Category choice = 2, Response = False
 Trial = 18, Category choice = 1, Response = Correct
-Trial = 19, Category choice = 2, Response = False
-Trial = 20, Category choice = 1, Response = False
+Trial = 19, Category choice = 1, Response = Correct
+Trial = 20, Category choice = 2, Response = Correct
 Trial = 21, Category choice = 1, Response = Correct
 Trial = 22, Category choice = 1, Response = Correct
-Trial = 23, Category choice = 2, Response = False
+Trial = 23, Category choice = 1, Response = Correct
 Trial = 24, Category choice = 2, Response = Correct
 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 = 28, Category choice = 2, Response = False
 Trial = 29, Category choice = 2, Response = Correct
 Trial = 30, Category choice = 1, Response = Correct
-Trial = 31, Category choice = 2, Response = Correct
+Trial = 31, Category choice = 1, Response = False
 Trial = 32, Category choice = 2, Response = False
-Trial = 33, Category choice = 1, Response = False
-Trial = 34, Category choice = 2, Response = Correct
+Trial = 33, Category choice = 2, Response = Correct
+Trial = 34, Category choice = 1, Response = False
 Trial = 35, Category choice = 1, Response = False
 Trial = 36, Category choice = 2, Response = False
-Trial = 37, Category choice = 1, Response = Correct
-Trial = 38, Category choice = 2, Response = False
+Trial = 37, Category choice = 2, Response = False
+Trial = 38, Category choice = 1, Response = Correct
 Trial = 39, Category choice = 2, Response = Correct
-Trial = 40, Category choice = 2, Response = False
+Trial = 40, Category choice = 1, Response = Correct
 Trial = 41, Category choice = 1, Response = False
-Trial = 42, Category choice = 2, Response = False
-Trial = 43, Category choice = 1, Response = Correct
+Trial = 42, Category choice = 1, Response = Correct
+Trial = 43, Category choice = 2, Response = False
 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 = 1, Response = Correct
-Trial = 48, Category choice = 1, Response = False
-Trial = 49, Category choice = 1, Response = False
+Trial = 45, Category choice = 1, Response = Correct
+Trial = 46, Category choice = 1, Response = Correct
+Trial = 47, Category choice = 2, Response = False
+Trial = 48, Category choice = 2, Response = Correct
+Trial = 49, Category choice = 2, Response = Correct
 Trial = 50, Category choice = 1, Response = False
-Trial = 51, Category choice = 1, Response = Correct
-Trial = 52, Category choice = 2, Response = False
-Trial = 53, Category choice = 2, Response = Correct
+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 = 1, Response = False
-Trial = 55, Category choice = 1, Response = False
+Trial = 55, Category choice = 2, Response = Correct
 Trial = 56, Category choice = 1, Response = False
 Trial = 57, Category choice = 1, Response = Correct
 Trial = 58, Category choice = 1, Response = Correct
-Trial = 59, Category choice = 1, Response = Correct
-Trial = 60, Category choice = 1, Response = Correct
-Trial = 61, Category choice = 2, Response = Correct
-Trial = 62, Category choice = 1, Response = False
-Trial = 63, Category choice = 2, Response = Correct
+Trial = 59, Category choice = 2, Response = False
+Trial = 60, Category choice = 2, Response = False
+Trial = 61, Category choice = 1, Response = False
+Trial = 62, Category choice = 2, Response = Correct
+Trial = 63, Category choice = 1, Response = False
 Trial = 64, Category choice = 1, Response = Correct
 Trial = 65, Category choice = 1, Response = Correct
 Trial = 66, Category choice = 1, Response = False
-Trial = 67, Category choice = 2, Response = False
+Trial = 67, Category choice = 1, Response = Correct
 Trial = 68, Category choice = 1, Response = False
-Trial = 69, Category choice = 1, Response = False
+Trial = 69, Category choice = 2, Response = Correct
 Trial = 70, Category choice = 2, Response = False
-Trial = 71, Category choice = 1, Response = Correct
-Trial = 72, Category choice = 1, Response = False
+Trial = 71, Category choice = 2, Response = False
+Trial = 72, Category choice = 2, Response = Correct
 Trial = 73, Category choice = 1, Response = False
-Trial = 74, Category choice = 1, Response = Correct
-Trial = 75, Category choice = 1, Response = Correct
+Trial = 74, Category choice = 2, Response = False
+Trial = 75, Category choice = 2, Response = False
 Trial = 76, Category choice = 2, Response = Correct
 Trial = 77, Category choice = 1, Response = Correct
 Trial = 78, Category choice = 1, Response = Correct
@@ -81,20 +81,20 @@ Trial = 80, Category choice = 1, Response = Correct
 Trial = 81, Category choice = 1, Response = False
 Trial = 82, Category choice = 2, Response = False
 Trial = 83, Category choice = 2, Response = False
-Trial = 84, Category choice = 2, Response = Correct
+Trial = 84, Category choice = 1, Response = False
 Trial = 85, Category choice = 1, Response = False
-Trial = 86, Category choice = 2, Response = False
-Trial = 87, Category choice = 2, Response = False
+Trial = 86, Category choice = 1, Response = Correct
+Trial = 87, Category choice = 1, Response = Correct
 Trial = 88, Category choice = 1, Response = Correct
-Trial = 89, Category choice = 1, Response = False
+Trial = 89, Category choice = 2, Response = Correct
 Trial = 90, Category choice = 2, Response = False
 Trial = 91, Category choice = 2, Response = Correct
-Trial = 92, Category choice = 2, Response = False
+Trial = 92, Category choice = 1, Response = Correct
 Trial = 93, Category choice = 1, Response = False
 Trial = 94, Category choice = 1, Response = False
-Trial = 95, Category choice = 2, Response = False
-Trial = 96, Category choice = 2, Response = Correct
-Trial = 97, Category choice = 2, Response = Correct
-Trial = 98, Category choice = 1, Response = False
+Trial = 95, Category choice = 1, Response = Correct
+Trial = 96, Category choice = 1, Response = False
+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

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[1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1], action = [2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1], action = [1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1])

pages/custom/index.html

@@ -49,7 +49,7 @@
 
         new(sys, namespace)
     <span class=hljs-keyword >end</span>
-<span class=hljs-keyword >end</span></code></pre> <blockquote> <p><strong><em>NOTE</em>:</strong> In <code>IzhNeuron</code> the <code>jcn</code> variable does not get a default value, only the &#91;input&#61;true&#93; tag. This means that other Bloxs will connect to a <code>IzhNeuron</code> through <code>jcn</code>.</p> <p>Neuroblox automatically initializes a <code>jcn ~ 0</code> equation and then accumulates connection terms in it. This happens with all input variables of Bloxs.</p> <p>Similarly the <code>&#91;output&#61;true&#93;</code> tag designates the <code>V</code> variable as the output variable. It is necessary for every Blox to have one if they rely on generic connection rules that fetch the output variable and add it to the connection equation.</p> <p>Both input and output tags are also useful to note which variables should be used when writing connection rules to or from our Blox.</p> </blockquote> <p>Now we are ready to define the first object of <code>IzhNeuron</code> and connect it with the <code>LIFNeuron</code> we created above.</p> <pre><code class="julia hljs"><span class=hljs-meta >@named</span> izh = IzhNeuron()</code></pre><pre><code class="plaintext hljs">IzhNeuron(ModelingToolkit.ODESystem(0x0000000000001fed, 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 =&gt; a, :b =&gt; b, :d =&gt; d, :V =&gt; V(t), :jcn =&gt; jcn, :u =&gt; u(t), :θ =&gt; θ, :V_reset =&gt; 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, &quot;&quot;, ModelingToolkit.ODESystem[], Dict{Any, Any}(a =&gt; 0.02, V_reset =&gt; -50, d =&gt; 2, u(t) =&gt; -13, V(t) =&gt; -65, b =&gt; 0.2, θ =&gt; 30), Dict{Any, Any}(), nothing, nothing, Symbolics.Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[condition: V(t) &gt; θ
+<span class=hljs-keyword >end</span></code></pre> <blockquote> <p><strong><em>NOTE</em>:</strong> In <code>IzhNeuron</code> the <code>jcn</code> variable does not get a default value, only the &#91;input&#61;true&#93; tag. This means that other Bloxs will connect to a <code>IzhNeuron</code> through <code>jcn</code>.</p> <p>Neuroblox automatically initializes a <code>jcn ~ 0</code> equation and then accumulates connection terms in it. This happens with all input variables of Bloxs.</p> <p>Similarly the <code>&#91;output&#61;true&#93;</code> tag designates the <code>V</code> variable as the output variable. It is necessary for every Blox to have one if they rely on generic connection rules that fetch the output variable and add it to the connection equation.</p> <p>Both input and output tags are also useful to note which variables should be used when writing connection rules to or from our Blox.</p> </blockquote> <p>Now we are ready to define the first object of <code>IzhNeuron</code> and connect it with the <code>LIFNeuron</code> we created above.</p> <pre><code class="julia hljs"><span class=hljs-meta >@named</span> izh = IzhNeuron()</code></pre><pre><code class="plaintext hljs">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 =&gt; a, :b =&gt; b, :d =&gt; d, :V =&gt; V(t), :jcn =&gt; jcn, :u =&gt; u(t), :θ =&gt; θ, :V_reset =&gt; 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, &quot;&quot;, ModelingToolkit.ODESystem[], Dict{Any, Any}(a =&gt; 0.02, V_reset =&gt; -50, d =&gt; 2, u(t) =&gt; -13, V(t) =&gt; -65, b =&gt; 0.2, θ =&gt; 30), Dict{Any, Any}(), nothing, nothing, Symbolics.Equation[], nothing, nothing, nothing, ModelingToolkit.SymbolicContinuousCallback[], ModelingToolkit.SymbolicDiscreteCallback[condition: V(t) &gt; θ
 affects:
   u(t) ~ d + u(t)
   V(t) ~ V_reset