Deploying to gh-pages from @ 2abbcdb 🚀

Manifest.toml

@@ -627,9 +627,9 @@ version = "1.11.0"
 
 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "4b138e4643b577ccf355377c2bc70fa975af25de"
+git-tree-sha1 = "7901a6117656e29fa2c74a58adb682f380922c47"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.115"
+version = "0.25.116"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"

assets/literate/DCM.md

@@ -293,7 +293,7 @@ Prepare the DCM. This function will setup the computation of the Dynamic Causal
 with_stack(f, n) = fetch(schedule(Task(f, n)));
 ````
 
-We are now ready to run the optimization procedure! :)
+We are now ready to run the optimization procedure!
 That is we loop over run_sDCM_iteration! which will alter `state` after each optimization iteration. It essentially computes the Variational Laplace estimation of expectation and variance of the tunable parameters.
 
 ````julia:ex26

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

@@ -1,4 +1,4 @@
-Main.FD_SANDBOX_13439752999271061286.IzhNeuron(ModelingToolkit.ODESystem(0x0000000000002013, 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(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
 affects:
   u(t) ~ d + u(t)
   V(t) ~ V_reset

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

@@ -1,100 +1,100 @@
-Trial = 1, Category choice = 1, Response = False
+Trial = 1, Category choice = 2, Response = Correct
 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 = 2, Response = False
-Trial = 6, Category choice = 2, Response = Correct
-Trial = 7, Category choice = 2, Response = Correct
-Trial = 8, Category choice = 2, Response = Correct
-Trial = 9, Category choice = 2, Response = Correct
+Trial = 5, Category choice = 1, Response = Correct
+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 = 1, Response = False
+Trial = 11, Category choice = 2, Response = Correct
 Trial = 12, Category choice = 1, Response = False
-Trial = 13, Category choice = 2, Response = False
-Trial = 14, Category choice = 2, 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 = 2, Response = False
+Trial = 13, Category choice = 1, Response = Correct
+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 = 18, Category choice = 1, Response = Correct
 Trial = 19, Category choice = 2, Response = False
-Trial = 20, Category choice = 2, Response = Correct
+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 = 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 = 25, Category choice = 1, Response = Correct
+Trial = 26, Category choice = 1, Response = Correct
 Trial = 27, Category choice = 2, Response = False
-Trial = 28, Category choice = 2, Response = False
+Trial = 28, Category choice = 1, Response = Correct
 Trial = 29, Category choice = 2, Response = Correct
 Trial = 30, Category choice = 1, Response = Correct
-Trial = 31, Category choice = 1, Response = False
-Trial = 32, Category choice = 1, Response = Correct
-Trial = 33, Category choice = 2, Response = Correct
+Trial = 31, Category choice = 2, Response = Correct
+Trial = 32, Category choice = 2, Response = False
+Trial = 33, Category choice = 1, Response = False
 Trial = 34, 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 = 38, Category choice = 1, Response = Correct
-Trial = 39, Category choice = 1, Response = False
-Trial = 40, Category choice = 1, Response = Correct
-Trial = 41, Category choice = 2, Response = Correct
+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 = 1, Response = False
 Trial = 42, Category choice = 2, Response = False
-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 = 43, 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 = 1, Response = Correct
-Trial = 48, Category choice = 2, Response = Correct
-Trial = 49, Category choice = 2, Response = Correct
+Trial = 48, Category choice = 1, Response = False
+Trial = 49, Category choice = 1, Response = False
 Trial = 50, Category choice = 1, Response = False
-Trial = 51, Category choice = 2, Response = False
+Trial = 51, Category choice = 1, Response = Correct
 Trial = 52, Category choice = 2, Response = False
 Trial = 53, Category choice = 2, Response = Correct
-Trial = 54, Category choice = 2, Response = Correct
-Trial = 55, Category choice = 2, Response = Correct
-Trial = 56, Category choice = 2, Response = Correct
-Trial = 57, Category choice = 2, Response = False
+Trial = 54, Category choice = 1, Response = False
+Trial = 55, Category choice = 1, Response = False
+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 = 2, Response = False
+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 = 64, Category choice = 2, Response = False
+Trial = 64, Category choice = 1, Response = Correct
 Trial = 65, Category choice = 1, Response = Correct
-Trial = 66, Category choice = 2, Response = Correct
+Trial = 66, Category choice = 1, Response = False
 Trial = 67, Category choice = 2, Response = False
-Trial = 68, Category choice = 2, Response = Correct
-Trial = 69, 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 = 71, Category choice = 1, Response = Correct
-Trial = 72, Category choice = 2, Response = Correct
+Trial = 72, Category choice = 1, Response = False
 Trial = 73, Category choice = 1, Response = False
-Trial = 74, Category choice = 2, Response = False
+Trial = 74, Category choice = 1, Response = Correct
 Trial = 75, Category choice = 1, Response = Correct
 Trial = 76, Category choice = 2, Response = Correct
-Trial = 77, Category choice = 2, Response = False
+Trial = 77, Category choice = 1, Response = Correct
 Trial = 78, Category choice = 1, Response = Correct
 Trial = 79, Category choice = 1, Response = Correct
-Trial = 80, Category choice = 2, Response = False
-Trial = 81, Category choice = 2, Response = Correct
-Trial = 82, Category choice = 1, Response = Correct
+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 = 1, Response = False
+Trial = 84, Category choice = 2, Response = Correct
 Trial = 85, Category choice = 1, Response = False
 Trial = 86, Category choice = 2, Response = False
 Trial = 87, Category choice = 2, Response = False
-Trial = 88, Category choice = 2, Response = False
-Trial = 89, Category choice = 2, Response = Correct
+Trial = 88, Category choice = 1, Response = Correct
+Trial = 89, Category choice = 1, Response = False
 Trial = 90, Category choice = 2, Response = False
 Trial = 91, Category choice = 2, Response = Correct
-Trial = 92, Category choice = 1, Response = Correct
-Trial = 93, Category choice = 2, Response = Correct
+Trial = 92, Category choice = 2, Response = False
+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 = 1, Response = False
-Trial = 97, Category choice = 1, Response = False
+Trial = 96, Category choice = 2, Response = Correct
+Trial = 97, Category choice = 2, Response = Correct
 Trial = 98, Category choice = 1, Response = False
-Trial = 99, Category choice = 2, Response = False
+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[0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1], action = [1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 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])

pages/DCM/index.html

@@ -300,7 +300,7 @@ <h2 id=setup_spectral_dcm ><a href="#setup_spectral_dcm" class=header-anchor >Se
 <span class=hljs-comment ># HACK: on machines with very small amounts of RAM, Julia can run out of stack space while compiling the code called in this loop</span>
 <span class=hljs-comment ># this should be rewritten to abuse the compiler less, but for now, an easy solution is just to run it with more allocated stack space.</span>
 with_stack(f, n) = fetch(schedule(<span class=hljs-built_in >Task</span>(f, n)));</code></pre>
-<p>We are now ready to run the optimization procedure&#33; :&#41; That is we loop over run<em>sDCM</em>iteration&#33; which will alter <code>state</code> after each optimization iteration. It essentially computes the Variational Laplace estimation of expectation and variance of the tunable parameters.</p>
+<p>We are now ready to run the optimization procedure&#33; That is we loop over run<em>sDCM</em>iteration&#33; which will alter <code>state</code> after each optimization iteration. It essentially computes the Variational Laplace estimation of expectation and variance of the tunable parameters.</p>
 <pre><code class="julia hljs">with_stack(<span class=hljs-number >5_000_000</span>) <span class=hljs-keyword >do</span>  <span class=hljs-comment ># 5MB of stack space</span>
     <span class=hljs-keyword >for</span> iter <span class=hljs-keyword >in</span> <span class=hljs-number >1</span>:max_iter
         state.iter = iter

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(0x0000000000002013, 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(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
 affects:
   u(t) ~ d + u(t)
   V(t) ~ V_reset