Rebuild models/08-spiking_neural_systems.jmd

markdown/models/08-spiking_neural_systems.md

@@ -4,7 +4,7 @@ title: "Spiking Neural Systems"
 ---
 
 
-This is an introduction to spiking neural systems with Julias DifferentialEquations package.
+This is an introduction to spiking neural systems with Julia's DifferentialEquations package.
 We will cover four different models: leaky integrate-and-fire, Izhikevich, adaptive exponential
  integrate-and-fire and Hodgkin-Huxley model. Let's get started with the leaky integrate-and-fire (LIF) model.
 
@@ -82,23 +82,24 @@ cb = CallbackSet(current_step,threshold)
 
 
 ````
-CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#61#64"{Arra
-y{Int64,1}},DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBox#333.var"#1#2"},
-DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Arra
-y{Int64,1},Main.##WeaveSandBox#333.var"#1#2"}},DiscreteCallback{typeof(Main
-.##WeaveSandBox#333.thr),typeof(Main.##WeaveSandBox#333.reset!),typeof(Diff
-EqBase.INITIALIZE_DEFAULT)}}}((), (DiscreteCallback{DiffEqCallbacks.var"#61
-#64"{Array{Int64,1}},DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBox#333.va
-r"#1#2"},DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),
-Bool,Array{Int64,1},Main.##WeaveSandBox#333.var"#1#2"}}(DiffEqCallbacks.var
-"#61#64"{Array{Int64,1}}([2, 15]), DiffEqCallbacks.var"#62#65"{Main.##Weave
-SandBox#333.var"#1#2"}(Main.##WeaveSandBox#333.var"#1#2"()), DiffEqCallback
-s.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Array{Int64,1},Mai
-n.##WeaveSandBox#333.var"#1#2"}(DiffEqBase.INITIALIZE_DEFAULT, true, [2, 15
-], Main.##WeaveSandBox#333.var"#1#2"()), Bool[1, 1]), DiscreteCallback{type
-of(Main.##WeaveSandBox#333.thr),typeof(Main.##WeaveSandBox#333.reset!),type
-of(DiffEqBase.INITIALIZE_DEFAULT)}(Main.##WeaveSandBox#333.thr, Main.##Weav
-eSandBox#333.reset!, DiffEqBase.INITIALIZE_DEFAULT, Bool[1, 1])))
+DiffEqBase.CallbackSet{Tuple{},Tuple{DiffEqBase.DiscreteCallback{DiffEqCall
+backs.var"#61#64"{Array{Int64,1}},DiffEqCallbacks.var"#62#65"{Main.##WeaveS
+andBox#321.var"#1#2"},DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INITIAL
+IZE_DEFAULT),Bool,Array{Int64,1},Main.##WeaveSandBox#321.var"#1#2"}},DiffEq
+Base.DiscreteCallback{typeof(Main.##WeaveSandBox#321.thr),typeof(Main.##Wea
+veSandBox#321.reset!),typeof(DiffEqBase.INITIALIZE_DEFAULT)}}}((), (DiffEqB
+ase.DiscreteCallback{DiffEqCallbacks.var"#61#64"{Array{Int64,1}},DiffEqCall
+backs.var"#62#65"{Main.##WeaveSandBox#321.var"#1#2"},DiffEqCallbacks.var"#6
+3#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Array{Int64,1},Main.##Weav
+eSandBox#321.var"#1#2"}}(DiffEqCallbacks.var"#61#64"{Array{Int64,1}}([2, 15
+]), DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBox#321.var"#1#2"}(Main.##W
+eaveSandBox#321.var"#1#2"()), DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase
+.INITIALIZE_DEFAULT),Bool,Array{Int64,1},Main.##WeaveSandBox#321.var"#1#2"}
+(DiffEqBase.INITIALIZE_DEFAULT, true, [2, 15], Main.##WeaveSandBox#321.var"
+#1#2"()), Bool[1, 1]), DiffEqBase.DiscreteCallback{typeof(Main.##WeaveSandB
+ox#321.thr),typeof(Main.##WeaveSandBox#321.reset!),typeof(DiffEqBase.INITIA
+LIZE_DEFAULT)}(Main.##WeaveSandBox#321.thr, Main.##WeaveSandBox#321.reset!,
+ DiffEqBase.INITIALIZE_DEFAULT, Bool[1, 1])))
 ````
 
 
@@ -247,23 +248,23 @@ cb = CallbackSet(current_step,threshold)
 
 
 ````
-CallbackSet{Tuple{},Tuple{DiscreteCallback{DiffEqCallbacks.var"#61#64"{Int6
-4},DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBox#333.var"#3#4"},DiffEqCal
-lbacks.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Int64,Main.##
-WeaveSandBox#333.var"#3#4"}},DiscreteCallback{typeof(Main.##WeaveSandBox#33
-3.thr),typeof(Main.##WeaveSandBox#333.reset!),typeof(DiffEqBase.INITIALIZE_
-DEFAULT)}}}((), (DiscreteCallback{DiffEqCallbacks.var"#61#64"{Int64},DiffEq
-Callbacks.var"#62#65"{Main.##WeaveSandBox#333.var"#3#4"},DiffEqCallbacks.va
-r"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Int64,Main.##WeaveSand
-Box#333.var"#3#4"}}(DiffEqCallbacks.var"#61#64"{Int64}(50), DiffEqCallbacks
-.var"#62#65"{Main.##WeaveSandBox#333.var"#3#4"}(Main.##WeaveSandBox#333.var
-"#3#4"()), DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT
-),Bool,Int64,Main.##WeaveSandBox#333.var"#3#4"}(DiffEqBase.INITIALIZE_DEFAU
-LT, true, 50, Main.##WeaveSandBox#333.var"#3#4"()), Bool[1, 1]), DiscreteCa
-llback{typeof(Main.##WeaveSandBox#333.thr),typeof(Main.##WeaveSandBox#333.r
-eset!),typeof(DiffEqBase.INITIALIZE_DEFAULT)}(Main.##WeaveSandBox#333.thr, 
-Main.##WeaveSandBox#333.reset!, DiffEqBase.INITIALIZE_DEFAULT, Bool[1, 1]))
-)
+DiffEqBase.CallbackSet{Tuple{},Tuple{DiffEqBase.DiscreteCallback{DiffEqCall
+backs.var"#61#64"{Int64},DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBox#32
+1.var"#3#4"},DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAU
+LT),Bool,Int64,Main.##WeaveSandBox#321.var"#3#4"}},DiffEqBase.DiscreteCallb
+ack{typeof(Main.##WeaveSandBox#321.thr),typeof(Main.##WeaveSandBox#321.rese
+t!),typeof(DiffEqBase.INITIALIZE_DEFAULT)}}}((), (DiffEqBase.DiscreteCallba
+ck{DiffEqCallbacks.var"#61#64"{Int64},DiffEqCallbacks.var"#62#65"{Main.##We
+aveSandBox#321.var"#3#4"},DiffEqCallbacks.var"#63#66"{typeof(DiffEqBase.INI
+TIALIZE_DEFAULT),Bool,Int64,Main.##WeaveSandBox#321.var"#3#4"}}(DiffEqCallb
+acks.var"#61#64"{Int64}(50), DiffEqCallbacks.var"#62#65"{Main.##WeaveSandBo
+x#321.var"#3#4"}(Main.##WeaveSandBox#321.var"#3#4"()), DiffEqCallbacks.var"
+#63#66"{typeof(DiffEqBase.INITIALIZE_DEFAULT),Bool,Int64,Main.##WeaveSandBo
+x#321.var"#3#4"}(DiffEqBase.INITIALIZE_DEFAULT, true, 50, Main.##WeaveSandB
+ox#321.var"#3#4"()), Bool[1, 1]), DiffEqBase.DiscreteCallback{typeof(Main.#
+#WeaveSandBox#321.thr),typeof(Main.##WeaveSandBox#321.reset!),typeof(DiffEq
+Base.INITIALIZE_DEFAULT)}(Main.##WeaveSandBox#321.thr, Main.##WeaveSandBox#
+321.reset!, DiffEqBase.INITIALIZE_DEFAULT, Bool[1, 1])))
 ````
 
 

notebook/models/08-spiking_neural_systems.ipynb

@@ -3,7 +3,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "This is an introduction to spiking neural systems with Julias DifferentialEquations package.\nWe will cover four different models: leaky integrate-and-fire, Izhikevich, adaptive exponential\n integrate-and-fire and Hodgkin-Huxley model. Let's get started with the leaky integrate-and-fire (LIF) model.\n\n## The Leaky-Integrate-and-Fire Model\nThe LIF model is an extension of the integrate-and-fire (IF) model. While the IF\nmodel simply integrates input until it fires, the LIF model integrates input but\nalso decays towards an equilibrium potential. This means that inputs that arrive\nin quick succession have a much higher chance to make the cell spike as opposed\nto inputs that are further apart in time. The LIF is a more realistic neuron\nmodel than the IF because it is known from real neurons that the timing of\ninputs is extremely relevant for their spiking.\n\nThe LIF model has four parameters, `gL, EL, C, Vth, I` and we define it in the `lif(u, p, t)` function."
+        "This is an introduction to spiking neural systems with Julia's DifferentialEquations package.\nWe will cover four different models: leaky integrate-and-fire, Izhikevich, adaptive exponential\n integrate-and-fire and Hodgkin-Huxley model. Let's get started with the leaky integrate-and-fire (LIF) model.\n\n## The Leaky-Integrate-and-Fire Model\nThe LIF model is an extension of the integrate-and-fire (IF) model. While the IF\nmodel simply integrates input until it fires, the LIF model integrates input but\nalso decays towards an equilibrium potential. This means that inputs that arrive\nin quick succession have a much higher chance to make the cell spike as opposed\nto inputs that are further apart in time. The LIF is a more realistic neuron\nmodel than the IF because it is known from real neurons that the timing of\ninputs is extremely relevant for their spiking.\n\nThe LIF model has four parameters, `gL, EL, C, Vth, I` and we define it in the `lif(u, p, t)` function."
       ],
       "metadata": {}
     },
@@ -247,11 +247,11 @@
       "file_extension": ".jl",
       "mimetype": "application/julia",
       "name": "julia",
-      "version": "1.5.1"
+      "version": "1.4.2"
     },
     "kernelspec": {
-      "name": "julia-1.5",
-      "display_name": "Julia 1.5.1",
+      "name": "julia-1.4",
+      "display_name": "Julia 1.4.2",
       "language": "julia"
     }
   },