time-based learning method based on new error formula

src/types.jl

@@ -325,7 +325,8 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
     # 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)
-    # Bn_wout_decay::Union{Float64,Nothing} = 0.01     # use to balance Bn and w_out
+    synapticStrength::Union{Array{Float64},Nothing} = nothing
+    synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(0=>0), upperlimit=(10=>10))
 
     gammaPd::Union{Float64,Nothing} = 0.3      # γ_pd, discount factor, value from paper
     alpha::Union{Float64,Nothing} = nothing          # α, neuron membrane potential decay factor
@@ -334,7 +335,6 @@ Base.@kwdef mutable struct lif_neuron <: compute_neuron
     decayedEpsilonRec::Union{Array{Float64},Nothing} = nothing     # α * epsilonRec
     eRec::Union{Array{Float64},Nothing} = nothing    # eligibility trace for neuron spike
     delta::Union{Float64,Nothing} = 1.0          # δ, discreate timestep size in millisecond
-    lastFiringTime::Union{Float64,Nothing} = 0.0   # the last time neuron fires, use to calculate exponantial decay of v_t1
     refractoryDuration::Union{Float64,Nothing} = 3     # neuron's refratory period in millisecond
     # refractory_state_active::Union{Bool,Nothing} = false         # if true, neuron is in refractory state and cannot process new information
     refractoryCounter::Integer = 0
@@ -418,7 +418,8 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
     # 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)
-    # Bn_wout_decay::Union{Float64,Nothing} = 0.01     # use to balance Bn and w_out
+    synapticStrength::Union{Array{Float64},Nothing} = nothing
+    synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(-5=>0), upperlimit=(5=>5))
 
     alpha::Union{Float64,Nothing} = nothing          # α, neuron membrane potential decay factor
     delta::Union{Float64,Nothing} = 1.0          # δ, discreate timestep size in millisecond
@@ -430,7 +431,6 @@ Base.@kwdef mutable struct alif_neuron <: compute_neuron
     eRec::Union{Array{Float64},Nothing} = nothing    # neuron's eligibility trace
     eta::Union{Float64,Nothing} = 0.01        # eta, learning rate
     gammaPd::Union{Float64,Nothing} = 0.3      # γ_pd, discount factor, value from paper
-    lastFiringTime::Union{Float64,Nothing} = 0.0   # the last time neuron fires, use to calculate exponantial decay of v_t1
     phi::Union{Float64,Nothing} = nothing          # ϕ, psuedo derivative
     refractoryDuration::Union{Float64,Nothing} = 3     # neuron's refractory period in millisecond
     # refractory_state_active::Union{Bool,Nothing} = false         # if true, neuron is in refractory state and cannot process new information
@@ -528,6 +528,8 @@ Base.@kwdef mutable struct linear_neuron <: output_neuron
     # neuron presynaptic firing at current timestep (which is other neuron postsynaptic firing of 
     # previous timestep)
     z_i_t::Union{Array{Bool},Nothing} = nothing 
+    synapticStrength::Union{Array{Float64},Nothing} = nothing
+    synapticStrengthLimit::Union{NamedTuple,Nothing} = (lowerlimit=(-5=>0), upperlimit=(5=>5))
 
     gammaPd::Union{Float64,Nothing} = 0.3      # γ_pd, discount factor, value from paper
     alpha::Union{Float64,Nothing} = nothing          # α, neuron membrane potential decay factor
@@ -536,7 +538,6 @@ Base.@kwdef mutable struct linear_neuron <: output_neuron
     decayedEpsilonRec::Union{Array{Float64},Nothing} = nothing     # α * epsilonRec
     eRec::Union{Array{Float64},Nothing} = nothing    # eligibility trace for neuron spike
     delta::Union{Float64,Nothing} = 1.0          # δ, discreate timestep size in millisecond
-    lastFiringTime::Union{Float64,Nothing} = 0.0   # the last time neuron fires, use to calculate exponantial decay of v_t1
     refractoryDuration::Union{Float64,Nothing} = 3     # neuron's refratory period in millisecond
     refractoryCounter::Integer = 0
     tau_out::Union{Float64,Nothing} = nothing        # τ_out, membrane time constant  in millisecond
@@ -629,11 +630,11 @@ function init_neuron!(id::Int64, n::lif_neuron, n_params::Dict, kfnParams::Dict)
     
     # prevent subscription to itself by removing this neuron id
     filter!(x -> x != n.id, n.subscriptionList)
+    n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
 
     n.epsilonRec = zeros(length(n.subscriptionList))
     n.w_rec = Random.rand(length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))
-    # n.reg_voltage_b = zeros(length(n.subscriptionList))
     n.alpha = calculate_α(n)
 end
 
@@ -648,6 +649,7 @@ function init_neuron!(id::Int64, n::alif_neuron, n_params::Dict,
 
     # prevent subscription to itself by removing this neuron id
     filter!(x -> x != n.id, n.subscriptionList)
+    n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
 
     n.epsilonRec = zeros(length(n.subscriptionList))
     n.w_rec = Random.rand(length(n.subscriptionList))
@@ -660,7 +662,7 @@ function init_neuron!(id::Int64, n::alif_neuron, n_params::Dict,
     n.epsilonRecA = zeros(length(n.subscriptionList))
 end
 
-#WORKING
+
 function init_neuron!(id::Int64, n::linear_neuron, n_params::Dict, kfnParams::Dict)
     n.id = id
     n.knowledgeFnName = kfnParams[:knowledgeFnName]
@@ -669,7 +671,7 @@ function init_neuron!(id::Int64, n::linear_neuron, n_params::Dict, kfnParams::Di
     subscription_numbers = Int(floor(n_params[:synaptic_connection_number] * 
                             kfnParams[:total_compute_neuron] / 100.0))
     n.subscriptionList = [pop!(subscription_options) for i = 1:subscription_numbers]
-
+    n.synapticStrength = normalize!(rand(length(n.subscriptionList)), 1)
     n.epsilonRec = zeros(length(n.subscriptionList))
     n.w_rec = Random.rand(length(n.subscriptionList))
     n.wRecChange = zeros(length(n.subscriptionList))