bug fix synapticConnStrength!()

src/forward.jl

@@ -116,6 +116,7 @@ function (n::lifNeuron)(kfn::knowledgeFn)
 
     # pulling other neuron's firing status at time t
     n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
+    n.z_i_t_commulative += n.z_i_t
 
     if n.refractoryCounter != 0
         n.refractoryCounter -= 1
@@ -157,6 +158,7 @@ function (n::alifNeuron)(kfn::knowledgeFn)
     n.timeStep = kfn.timeStep
 
     n.z_i_t = getindex(kfn.firedNeurons_t0, n.subscriptionList)
+    n.z_i_t_commulative += n.z_i_t
 
     if n.refractoryCounter != 0
         n.refractoryCounter -= 1
@@ -204,6 +206,7 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
 
     # pulling other neuron's firing status at time t
     n.z_i_t = getindex(kfn.firedNeurons_t1, n.subscriptionList)
+    n.z_i_t_commulative += n.z_i_t
 
     if n.refractoryCounter != 0
         n.refractoryCounter -= 1

src/learn.jl

@@ -49,9 +49,9 @@ function learn!(kfn::kfn_1, correctAnswer::AbstractVector)
                 wSign_1 = sign.(n.wRec)   # check for fliped sign, 1 indicates non-fliped sign
                 nonFlipedSign = isequal.(wSign_0, wSign_1)  # 1 not fliped, 0 fliped
                 # normalize wRec peak to prevent input signal overwhelming neuron
-                normalizePeak!(n.wRec, 2)
+                normalizePeak!(n.wRec, n.wRecChange, 2)
-                n.wRec .*= nonFlipedSign  # set weight that fliped sign to 0 for random new connection
+                 # set weight that fliped sign to 0 for random new connection
-
+                n.wRec .*= nonFlipedSign 
 
                 synapticConnStrength!(n)
                 #TODO neuroplasticity

src/snn_utils.jl

@@ -46,6 +46,7 @@ reset_reg_voltage_error!(n::computeNeuron) = n.reg_voltage_error = n.reg_voltage
 reset_firing_counter!(n::Union{computeNeuron, outputNeuron}) = n.firingCounter = n.firingCounter * 0.0
 reset_firing_diff!(n::Union{computeNeuron, linearNeuron}) = n.firingDiff = n.firingDiff * 0.0
 reset_refractoryCounter!(n::Union{computeNeuron, outputNeuron}) = n.refractoryCounter = n.refractoryCounter * 0.0
+reset_z_i_t_commulative!(n::Union{computeNeuron, outputNeuron}) = n.z_i_t_commulative = n.z_i_t_commulative * 0.0
 
 # reset function for output neuron
 reset_epsilon_j!(n::linearNeuron) = n.epsilon_j = n.epsilon_j * 0.0
@@ -109,7 +110,8 @@ function resetLearningParams!(n::lifNeuron)
     
     # reset refractory state at the start/end of episode. Otherwise once neuron goes into 
     # refractory state, it will stay in refractory state forever 
-    reset_refractoryCounter!(n)
+    # reset_refractoryCounter!(n)
+    reset_z_i_t_commulative!(n)
 end
 function resetLearningParams!(n::alifNeuron)
     reset_epsilonRec!(n)
@@ -124,6 +126,7 @@ function resetLearningParams!(n::alifNeuron)
     # reset refractory state at the start/end of episode. Otherwise once neuron goes into 
     # refractory state, it will stay in refractory state forever 
     # reset_refractoryCounter!(n) 
+    reset_z_i_t_commulative!(n)
 end
 
 # function reset_learning_no_wchange!(n::passthroughNeuron)
@@ -142,6 +145,7 @@ function resetLearningParams!(n::linearNeuron)
     # reset refractory state at the start/end of episode. Otherwise once neuron goes into 
     # refractory state, it will stay in refractory state forever 
     # reset_refractoryCounter!(n)
+    reset_z_i_t_commulative!(n)
 end
 #------------------------------------------------------------------------------------------------100
 
@@ -279,8 +283,14 @@ end
 function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
     for (i, connStrength) in enumerate(n.synapticStrength)
         # check whether connStrength increase or decrease based on usage from n.epsilonRec
-        #WORKING  n.epsilonRec is all 0.0  why? may b it was reset? ANS: model fire at this timestep and gets reset epsilonRec during ΔwRecChange compute
+        """ use n.wRecChange instead of the best choise, epsilonRec, here because ΔwRecChange 
-        updown = n.epsilonRec[i] == 0.0 ? "down" : "up" 
+                calculation in learn!() will reset epsilonRec to zeroes vector in case where 
+                output neuron fires and trigger learn!() just before this synapticConnStrength 
+                calculation.
+            Since n.wRecChange indicates whether a synaptic connection were used or not, it is
+                ok to use. n.wRecChange also span across a training sample without resetting.
+        """
+        updown = n.z_i_t_commulative[i] == 0 ? "down" : "up" # 
         updatedConnStrength = synapticConnStrength(connStrength, updown)
         updatedConnStrength = GeneralUtils.limitvalue(updatedConnStrength, 
                         n.synapticStrengthLimit.lowerlimit, n.synapticStrengthLimit.upperlimit)               
@@ -288,6 +298,7 @@ function synapticConnStrength!(n::Union{computeNeuron, outputNeuron})
         if updatedConnStrength == n.synapticStrengthLimit.lowerlimit[1]        
             n.wRec[i] = 0.0
         end
+        n.synapticStrength[i] = updatedConnStrength
     end
 end
 
@@ -319,15 +330,16 @@ function neuroplasticity!(n::computeNeuron, firedNeurons::Vector)
 end
 
 """ normalize a part of a vector centering at a vector's maximum value along with nearby value
-    within its radius. radius must be odd number
+    within its radius. radius must be odd number.
+    v1 will be normalized based on v2's peak
 """
-function normalizePeak!(v::Vector, radius::Integer=2)
+function normalizePeak!(v1::Vector, v2::Vector, radius::Integer=2)
-    peak = findall(isequal.(abs.(v), maximum(abs.(v))))[1]
+    peak = findall(isequal.(abs.(v2), maximum(abs.(v2))))[1]
     upindex = peak - radius
     upindex = upindex < 1 ? 1 : upindex
     downindex = peak + radius
-    downindex = downindex > length(v) ? length(v) : downindex
+    downindex = downindex > length(v1) ? length(v1) : downindex
-    subvector = view(v, upindex:downindex)
+    subvector = view(v1, upindex:downindex)
     normalize!(subvector, 1)
 end
 

src/types.jl

@@ -322,6 +322,7 @@ Base.@kwdef mutable struct lifNeuron <: computeNeuron
     # during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
     z_t1::Bool = false    # neuron postsynaptic firing at current timestep (after neuron's calculation)
     z_i_t::Union{Array{Bool},Nothing} = nothing    # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep)
+    z_i_t_commulative::Union{Array{Integer},Nothing} = nothing     # used to compute connection strength
     synapticStrength::Union{Array{Float64},Nothing} = nothing
     synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(0=>0), upperlimit=(10=>10))
 
@@ -413,6 +414,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
     # during v_t1 calculation hence I need a variable to hold z_t1 so that I'm not replacing z_t
     z_t1::Bool = false    # neuron postsynaptic firing at current timestep (after neuron's calculation)
     z_i_t::Union{Array{Bool},Nothing} = nothing    # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of previous timestep)
+    z_i_t_commulative::Union{Array{Integer},Nothing} = nothing     # used to compute connection strength
     synapticStrength::Union{Array{Float64},Nothing} = nothing
     synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(-5=>0), upperlimit=(5=>5))
 
@@ -523,6 +525,7 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
     # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of 
     # previous timestep)
     z_i_t::Union{Array{Bool},Nothing} = nothing 
+    z_i_t_commulative::Union{Array{Integer},Nothing} = nothing     # used to compute connection strength
     synapticStrength::Union{Array{Float64},Nothing} = nothing
     synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(-5=>-5), upperlimit=(5=>5))
 
@@ -630,6 +633,7 @@ function init_neuron!(id::Int64, n::lifNeuron, n_params::Dict, kfnParams::Dict)
     n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
     n.alpha = calculate_α(n)
+    n.z_i_t_commulative = zeros(length(n.subscriptionList))
 end
 
 function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict, 
@@ -654,6 +658,7 @@ function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
     n.alpha = calculate_α(n)
     n.rho = calculate_ρ(n)
     n.epsilonRecA = zeros(length(n.subscriptionList))
+    n.z_i_t_commulative = zeros(length(n.subscriptionList))
 end
 
 
@@ -671,6 +676,7 @@ function init_neuron!(id::Int64, n::linearNeuron, n_params::Dict, kfnParams::Dic
     n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
     n.alpha = calculate_k(n)
+    n.z_i_t_commulative = zeros(length(n.subscriptionList))
 end
 
 """ Make a neuron intended for use with knowledgeFn