building v0.0.6

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