building v0.0.6

src/Ironpen.jl

@@ -34,7 +34,8 @@ using .learn
 
 """ version 0.0.6
     Todo:
-        [1] use abs(wRec) suring neuron init
+        [*1] if neuron not fire for a long time, reduce it conn strength
+        [DONE] use abs(wRec) during neuron init
         [2] implement dormant connection and pruning machanism. the longer the training the longer 
             0 weight stay 0. 
         [] using RL to control learning signal

src/forward.jl

@@ -60,7 +60,7 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
     end
 
     # generate noise
-    noise = [GeneralUtils.randomChoiceWithProb([true, false],[0.0, 1.0])
+    noise = [GeneralUtils.randomChoiceWithProb([true, false],[0.2, 0.8])
                 for i in 1:length(input_data)]
     # noise = [rand(rng, Distributions.Binomial(1, 0.5)) for i in 1:10]     # another option
 
@@ -95,7 +95,7 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
 
     return  sum(kfn.firedNeurons_t1[kfn.kfnParams[:totalInputPort]+1:end])::Int,
             logit::Array{Float64},
-            [n.v_t1 for n in kfn.outputNeuronsArray], 
+            [i for i in kfn.neuronsArray[end].wRec[1:10]], 
             [sum(i.wRec) for i in kfn.outputNeuronsArray],
             [sum(i.epsilonRec) for i in kfn.outputNeuronsArray],
             [sum(i.wRecChange) for i in kfn.outputNeuronsArray]

src/learn.jl

@@ -71,7 +71,7 @@ function compute_wRecChange!(n::lifNeuron, wOut::AbstractVector, modelError::Flo
     #     ΔwRecChange .+= (0.2*(abs(sum(n.wRec)) / length(n.wRec)))
     # end
     n.wRecChange .+= ΔwRecChange
-    # reset_epsilonRec!(n)
+    reset_epsilonRec!(n)
 end
 
 function compute_wRecChange!(n::alifNeuron, wOut::AbstractVector, modelError::Float64)
@@ -88,8 +88,8 @@ function compute_wRecChange!(n::alifNeuron, wOut::AbstractVector, modelError::Fl
     # end
     n.wRecChange .+= ΔwRecChange
 
-    # reset_epsilonRec!(n)
+    reset_epsilonRec!(n)
-    # reset_epsilonRecA!(n)
+    reset_epsilonRecA!(n)
     # n.alphaChange += compute_alphaChange(n.eta, nError)
 end
 
@@ -101,7 +101,7 @@ function compute_wRecChange!(n::integrateNeuron, error::Float64)
     # end
     n.wRecChange .+= ΔwRecChange
     n.bChange += ΔbChange
-    # reset_epsilonRec!(n)
+    reset_epsilonRec!(n)
     # n.alphaChange += compute_alphaChange(n.eta, error)
 end
 
@@ -165,8 +165,21 @@ function learn!(n::T, firedNeurons, nExInType) where T<:computeNeuron
     # end
     # set weight that fliped sign to 0 for random new connection
     n.wRec .*= nonFlipedSign 
-    # capMaxWeight!(n.wRec)   # cap maximum weight
+    # n.wRec = wRecMaxWeight!(n, max=1.0)   # cap maximum weight
+
+    # check for non firing. if neuron not fire for too long, reduce all connection strength
+    if n.id ∈ firedNeurons
+        n.notFireCounter = n.notFireTimeOut
         synapticConnStrength!(n, "updown")
+    elseif n.id ∉ firedNeurons && n.notFireCounter != n.notFireTimeOut
+        n.notFireTimeOut += 1
+        synapticConnStrength!(n, "updown")
+    elseif n.id ∉ firedNeurons && n.notFireCounter == n.notFireCounter
+        synapticConnStrength!(n, "down")
+    else
+        error("undefined condition line $(@__LINE__)")
+    end
+
     neuroplasticity!(n, firedNeurons, nExInType)
 end
 

src/snn_utils.jl

@@ -6,7 +6,7 @@ export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron,
         reset_epsilonRecA!, synapticConnStrength!, normalizePeak!, reset_wRecChange!,
         firing_rate_error!, firing_rate_regulator!, update_Bn!, cal_firing_reg!,
         neuroplasticity!, shakeup!, reset_learning_no_wchange!, adjust_internal_learning_rate!,
-        gradient_withloss, capMaxWeight!, connStrengthAdjust
+        gradient_withloss, capMaxWeight, connStrengthAdjust, wRecMaxWeight!
 
 using Statistics, Random, LinearAlgebra, Distributions, Zygote, Flux
 using GeneralUtils
@@ -404,9 +404,14 @@ end
 
 """ Cap maximum weight of each neuron connection
 """
-function capMaxWeight!(v::Vector{Float64}, max=1.0)
+function capMaxWeight(v::Vector{Float64}, max=1.0)
     originalSign = sign.(v)
-    v = originalSign .* GeneralUtils.replaceMoreThan.(abs.(v), max)
+    return originalSign .* GeneralUtils.replaceMoreThan.(abs.(v), max)
+end
+
+function wRecMaxWeight!(n::computeNeuron; max=1.0)
+    originalSign = sign.(n.wRec)
+    n.wRec = originalSign .* GeneralUtils.replaceMoreThan.(abs.(n.wRec), max)
 end
 
 
@@ -425,11 +430,6 @@ end
 
 
 
-
-
-
-
-
 
 
 

src/types.jl

@@ -253,7 +253,7 @@ function kfn_1(kfnParams::Dict)
         try     # input neuron doest have n.subscriptionList
             for (i, sub_id) in enumerate(n.subscriptionList)
                 n_ExInType = kfn.neuronsArray[sub_id].ExInType
-                n.wRec[i] *= n_ExInType
+                n.wRec[i] = abs(n.wRec[i]) * n_ExInType
                 # add id  exin type to kfn
                 if n_ExInType < 0
                     push!(kfn.nInhabitory, sub_id)
@@ -364,6 +364,9 @@ Base.@kwdef mutable struct lifNeuron <: computeNeuron
     firingRateError::Float64 = 0.0  # local neuron error w.r.t. firing regularization
     firingRate::Float64 = 0.0 # running average of firing rate in Hz
 
+    notFireTimeOut::Int64 = 100   # consecutive count of not firing. Should be the same as batch size
+    notFireCounter::Int64 = 0
+
     """ "inference" = no learning params will be collected.
         "learning" = neuron will accumulate epsilon_j, compute Δw_rec_change each time
                     correct answer is available then merge Δw_rec_change into wRecChange then
@@ -458,6 +461,9 @@ 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
 
+    notFireTimeOut::Int64 = 100   # consecutive count of not firing. Should be the same as batch size
+    notFireCounter::Int64 = 0
+
     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
@@ -744,7 +750,7 @@ function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
     n.synapticStrength = rand(-4.5:0.01:-4, length(n.subscriptionList))
 
     n.epsilonRec = zeros(length(n.subscriptionList))
-    n.wRec = randn(rng, length(n.subscriptionList)) / 100   # TODO use abs()
+    n.wRec = randn(rng, length(n.subscriptionList)) / 100
     n.wRecChange = zeros(length(n.subscriptionList))
 
     # the more time has passed from the last time neuron was activated, the more