update version

src/Ironpen.jl

@@ -32,8 +32,9 @@ using .learn
 # using .interface
 #------------------------------------------------------------------------------------------------100
 
-""" version 0.0.4
+""" version 0.0.5
     Todo:
+        - 
         [4] implement dormant connection
         [] using RL to control learning signal
         [] consider using Dates.now() instead of timestamp because time_stamp may overflow
@@ -41,8 +42,9 @@ using .learn
             which defined by neuron.tau_m formula in type.jl
 
 
-    Change from version:    0.0.3
+    Change from version:    0.0.4
-        
+        - compute error in main loop so one could decide how to calculate error
+        - compute model error in main loop so one could decide when to calculate error
 
     
     All features

src/forward.jl

@@ -90,10 +90,13 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
     end
 
     out = [n.z_t1 for n in kfn.outputNeuronsArray]
-    outputNeuron_v_t1 = [n.v_t1 for n in kfn.outputNeuronsArray]
 
-    return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}, sum(kfn.firedNeurons_t1),
+    return out::Array{Bool}, 
-            [i.ExInSignalSum for i in kfn.outputNeuronsArray]
+            sum(kfn.firedNeurons_t1),
+            [n.v_t1 for n in kfn.outputNeuronsArray], 
+            [sum(i.wRec) for i in kfn.outputNeuronsArray],
+            [sum(i.epsilonRec) for i in kfn.outputNeuronsArray],
+            [i.phi for i in kfn.outputNeuronsArray]
 end
 
 #------------------------------------------------------------------------------------------------100
@@ -218,16 +221,6 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
         n.vError = n.v_t1   # store voltage that will be used to calculate error later
     else
         recSignal = n.wRec .* n.z_i_t
-        n.ExInSignalSum = 0.0   #CHANGE for ploting
-        for i in recSignal
-            # if i > 0
-            #     kfn.exSignalSum += i
-            # elseif i < 0
-            #     kfn.inSignalsum += i
-            # else
-            # end
-            n.ExInSignalSum += i
-        end
         n.recSignal = sum(recSignal)  # signal from other neuron that this neuron subscribed
         n.alpha_v_t = n.alpha * n.v_t
         n.v_t1 = n.alpha_v_t + n.recSignal

src/learn.jl

@@ -19,31 +19,51 @@ function compute_wRecChange!(m::model, error::Vector{Float64}, correctAnswer::Ab
     compute_wRecChange!(m.knowledgeFn[:I], error, correctAnswer)
 end
 
+# function compute_wRecChange!(kfn::kfn_1, errors::Vector{Float64}, correctAnswer::AbstractVector)
+#     for (i, error) in enumerate(errors)
+#         if error == 0  # output is correct
+#             # Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
+#             # # for n in kfn.neuronsArray
+#             #     synapticConnStrength!(n, true)
+#             # end
+#             # synapticConnStrength!(kfn.outputNeuronsArray[i], true)
+#         else    # output is wrong, error occurs
+#             if correctAnswer[i] == 1  # high priority answer
+#                 error = error * 
+#                         abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
+#             else    # low priority answer
+#                 error = error *
+#                         abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
+#             end
+            
+#             Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
+#             # for n in kfn.neuronsArray
+#                 compute_wRecChange!(n, error)
+#                 # synapticConnStrength!(n, false)
+#             end
+#             compute_wRecChange!(kfn.outputNeuronsArray[i], error)
+#             # synapticConnStrength!(kfn.outputNeuronsArray[i], false)
+#         end
+#     end
+
+
 function compute_wRecChange!(kfn::kfn_1, errors::Vector{Float64}, correctAnswer::AbstractVector)
     for (i, error) in enumerate(errors)
-        if error == 0  # output is correct
+        if error < 0    # model fires too fast
-            # Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
+            error = error *
-            # # for n in kfn.neuronsArray
-            #     synapticConnStrength!(n, true)
-            # end
-            # synapticConnStrength!(kfn.outputNeuronsArray[i], true)
-        else    # output is wrong, error occurs
-            if correctAnswer[i] == 1  # high priority answer
-                error = error * 
                         abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
-            else    # low priority answer
+        elseif error == 0   # model answer correctly. maintain membrain potential ≈ 0.5
-                error = error *
+            error = error *
+                        abs(kfn.outputNeuronsArray[i].v_th/2 - kfn.outputNeuronsArray[i].vError)
+        else    # model fires too slow
+            error = error *
                         abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
-            end
-            
-            Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
-            # for n in kfn.neuronsArray
-                compute_wRecChange!(n, error)
-                # synapticConnStrength!(n, false)
-            end
-            compute_wRecChange!(kfn.outputNeuronsArray[i], error)
-            # synapticConnStrength!(kfn.outputNeuronsArray[i], false)
         end
+        Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
+        # for n in kfn.neuronsArray
+            compute_wRecChange!(n, error)
+        end
+        compute_wRecChange!(kfn.outputNeuronsArray[i], error)
     end
 end
 

src/types.jl

@@ -346,7 +346,7 @@ Base.@kwdef mutable struct lifNeuron <: computeNeuron
     delta::Float64 = 1.0          # δ, discreate timestep size in millisecond
     refractoryDuration::Int64 = 3     # neuron's refratory period in millisecond
     refractoryCounter::Int64 = 0
-    tau_m::Float64 = 50.0        # τ_m, membrane time constant  in millisecond
+    tau_m::Float64 = 100.0        # τ_m, membrane time constant  in millisecond
     eta::Float64 = 0.01        # η, learning rate
     wRecChange::Array{Float64} = Float64[]     # Δw_rec, cumulated wRec change
     recSignal::Float64 = 0.0    # incoming recurrent signal
@@ -440,7 +440,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
     phi::Float64 = 0.0          # ϕ, psuedo derivative
     refractoryDuration::Int64 = 3     # neuron's refractory period in millisecond
     refractoryCounter::Int64 = 0
-    tau_m::Float64 = 50.0        # τ_m, membrane time constant  in millisecond 
+    tau_m::Float64 = 100.0        # τ_m, membrane time constant  in millisecond 
     wRecChange::Array{Float64} = Float64[]     # Δw_rec, cumulated wRec change
     recSignal::Float64 = 0.0    # incoming recurrent signal
     alpha_v_t::Float64 = 0.0  # alpha * v_t
@@ -453,7 +453,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
     firingRateError::Float64 = 0.0  # local neuron error w.r.t. firing regularization
     firingRate::Float64 = 0.0 # running average of firing rate, Hz
 
-    tau_a::Float64 = 50.0  # τ_a, adaption time constant in millisecond
+    tau_a::Float64 = 100.0  # τ_a, adaption time constant in millisecond
     beta::Float64 = 0.15  # β, constant, value from paper
     rho::Float64 = 0.0  # ρ, threshold adaptation decay factor
     a::Float64 = 0.0  # threshold adaptation
@@ -544,7 +544,7 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
     delta::Float64 = 1.0          # δ, discreate timestep size in millisecond
     refractoryDuration::Int64 = 3     # neuron's refratory period in millisecond
     refractoryCounter::Int64 = 0
-    tau_out::Float64 = 25.0        # τ_out, membrane time constant  in millisecond
+    tau_out::Float64 = 50.0        # τ_out, membrane time constant  in millisecond
     eta::Float64 = 0.01        # η, learning rate
     wRecChange::Array{Float64} = Float64[]     # Δw_rec, cumulated wRec change
     recSignal::Float64 = 0.0    # incoming recurrent signal
@@ -637,7 +637,7 @@ function init_neuron!(id::Int64, n::lifNeuron, n_params::Dict, kfnParams::Dict)
     n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
 
     n.epsilonRec = zeros(length(n.subscriptionList))
-    n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
+    n.wRec = randn(length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
     n.alpha = calculate_α(n)
     n.z_i_t_commulative = zeros(length(n.subscriptionList))
@@ -657,7 +657,7 @@ function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
     n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
 
     n.epsilonRec = zeros(length(n.subscriptionList))
-    n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
+    n.wRec = randn(length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
 
     # the more time has passed from the last time neuron was activated, the more 
@@ -680,7 +680,7 @@ function init_neuron!(id::Int64, n::linearNeuron, n_params::Dict, kfnParams::Dic
     n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
 
     n.epsilonRec = zeros(length(n.subscriptionList))
-    n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
+    n.wRec = randn(length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
     n.alpha = calculate_k(n)
     n.z_i_t_commulative = zeros(length(n.subscriptionList))