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@@ -32,8 +32,9 @@ using .learn
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# using .interface
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#------------------------------------------------------------------------------------------------100
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""" version 0.0.4
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""" version 0.0.5
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Todo:
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-
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[4] implement dormant connection
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[] using RL to control learning signal
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[] consider using Dates.now() instead of timestamp because time_stamp may overflow
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@@ -41,8 +42,9 @@ using .learn
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which defined by neuron.tau_m formula in type.jl
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Change from version: 0.0.3
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Change from version: 0.0.4
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- compute error in main loop so one could decide how to calculate error
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- compute model error in main loop so one could decide when to calculate error
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All features
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@@ -90,10 +90,13 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
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end
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out = [n.z_t1 for n in kfn.outputNeuronsArray]
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outputNeuron_v_t1 = [n.v_t1 for n in kfn.outputNeuronsArray]
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return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}, sum(kfn.firedNeurons_t1),
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[i.ExInSignalSum for i in kfn.outputNeuronsArray]
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return out::Array{Bool},
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sum(kfn.firedNeurons_t1),
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[n.v_t1 for n in kfn.outputNeuronsArray],
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[sum(i.wRec) for i in kfn.outputNeuronsArray],
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[sum(i.epsilonRec) for i in kfn.outputNeuronsArray],
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[i.phi for i in kfn.outputNeuronsArray]
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end
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#------------------------------------------------------------------------------------------------100
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@@ -218,16 +221,6 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
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n.vError = n.v_t1 # store voltage that will be used to calculate error later
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else
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recSignal = n.wRec .* n.z_i_t
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n.ExInSignalSum = 0.0 #CHANGE for ploting
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for i in recSignal
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# if i > 0
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# kfn.exSignalSum += i
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# elseif i < 0
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# kfn.inSignalsum += i
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# else
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# end
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n.ExInSignalSum += i
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end
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n.recSignal = sum(recSignal) # signal from other neuron that this neuron subscribed
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n.alpha_v_t = n.alpha * n.v_t
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n.v_t1 = n.alpha_v_t + n.recSignal
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44
src/learn.jl
44
src/learn.jl
@@ -19,31 +19,51 @@ function compute_wRecChange!(m::model, error::Vector{Float64}, correctAnswer::Ab
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compute_wRecChange!(m.knowledgeFn[:I], error, correctAnswer)
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end
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function compute_wRecChange!(kfn::kfn_1, errors::Vector{Float64}, correctAnswer::AbstractVector)
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for (i, error) in enumerate(errors)
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if error == 0 # output is correct
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# function compute_wRecChange!(kfn::kfn_1, errors::Vector{Float64}, correctAnswer::AbstractVector)
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# for (i, error) in enumerate(errors)
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# if error == 0 # output is correct
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# # Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
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# # # for n in kfn.neuronsArray
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# # synapticConnStrength!(n, true)
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# # end
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# # synapticConnStrength!(kfn.outputNeuronsArray[i], true)
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# else # output is wrong, error occurs
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# if correctAnswer[i] == 1 # high priority answer
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# error = error *
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# abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
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# else # low priority answer
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# error = error *
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# abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
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# end
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# Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
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# # for n in kfn.neuronsArray
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# synapticConnStrength!(n, true)
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# compute_wRecChange!(n, error)
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# # synapticConnStrength!(n, false)
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# end
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# synapticConnStrength!(kfn.outputNeuronsArray[i], true)
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else # output is wrong, error occurs
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if correctAnswer[i] == 1 # high priority answer
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# compute_wRecChange!(kfn.outputNeuronsArray[i], error)
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# # synapticConnStrength!(kfn.outputNeuronsArray[i], false)
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# end
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# end
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function compute_wRecChange!(kfn::kfn_1, errors::Vector{Float64}, correctAnswer::AbstractVector)
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for (i, error) in enumerate(errors)
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if error < 0 # model fires too fast
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error = error *
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abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
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else # low priority answer
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elseif error == 0 # model answer correctly. maintain membrain potential ≈ 0.5
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error = error *
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abs(kfn.outputNeuronsArray[i].v_th/2 - kfn.outputNeuronsArray[i].vError)
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else # model fires too slow
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error = error *
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abs(kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError)
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end
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Threads.@threads for n in kfn.neuronsArray # multithread is not atomic and causing error
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# for n in kfn.neuronsArray
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compute_wRecChange!(n, error)
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# synapticConnStrength!(n, false)
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end
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compute_wRecChange!(kfn.outputNeuronsArray[i], error)
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# synapticConnStrength!(kfn.outputNeuronsArray[i], false)
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end
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end
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end
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14
src/types.jl
14
src/types.jl
@@ -346,7 +346,7 @@ Base.@kwdef mutable struct lifNeuron <: computeNeuron
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delta::Float64 = 1.0 # δ, discreate timestep size in millisecond
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refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
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refractoryCounter::Int64 = 0
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tau_m::Float64 = 50.0 # τ_m, membrane time constant in millisecond
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tau_m::Float64 = 100.0 # τ_m, membrane time constant in millisecond
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eta::Float64 = 0.01 # η, learning rate
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wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
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recSignal::Float64 = 0.0 # incoming recurrent signal
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@@ -440,7 +440,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
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phi::Float64 = 0.0 # ϕ, psuedo derivative
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refractoryDuration::Int64 = 3 # neuron's refractory period in millisecond
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refractoryCounter::Int64 = 0
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tau_m::Float64 = 50.0 # τ_m, membrane time constant in millisecond
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tau_m::Float64 = 100.0 # τ_m, membrane time constant in millisecond
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wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
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recSignal::Float64 = 0.0 # incoming recurrent signal
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alpha_v_t::Float64 = 0.0 # alpha * v_t
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@@ -453,7 +453,7 @@ Base.@kwdef mutable struct alifNeuron <: computeNeuron
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firingRateError::Float64 = 0.0 # local neuron error w.r.t. firing regularization
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firingRate::Float64 = 0.0 # running average of firing rate, Hz
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tau_a::Float64 = 50.0 # τ_a, adaption time constant in millisecond
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tau_a::Float64 = 100.0 # τ_a, adaption time constant in millisecond
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beta::Float64 = 0.15 # β, constant, value from paper
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rho::Float64 = 0.0 # ρ, threshold adaptation decay factor
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a::Float64 = 0.0 # threshold adaptation
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@@ -544,7 +544,7 @@ Base.@kwdef mutable struct linearNeuron <: outputNeuron
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delta::Float64 = 1.0 # δ, discreate timestep size in millisecond
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refractoryDuration::Int64 = 3 # neuron's refratory period in millisecond
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refractoryCounter::Int64 = 0
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tau_out::Float64 = 25.0 # τ_out, membrane time constant in millisecond
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tau_out::Float64 = 50.0 # τ_out, membrane time constant in millisecond
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eta::Float64 = 0.01 # η, learning rate
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wRecChange::Array{Float64} = Float64[] # Δw_rec, cumulated wRec change
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recSignal::Float64 = 0.0 # incoming recurrent signal
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@@ -637,7 +637,7 @@ function init_neuron!(id::Int64, n::lifNeuron, n_params::Dict, kfnParams::Dict)
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n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
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n.epsilonRec = zeros(length(n.subscriptionList))
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n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
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n.wRec = randn(length(n.subscriptionList))
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n.wRecChange = zeros(length(n.subscriptionList))
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n.alpha = calculate_α(n)
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n.z_i_t_commulative = zeros(length(n.subscriptionList))
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@@ -657,7 +657,7 @@ function init_neuron!(id::Int64, n::alifNeuron, n_params::Dict,
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n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
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n.epsilonRec = zeros(length(n.subscriptionList))
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n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
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n.wRec = randn(length(n.subscriptionList))
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n.wRecChange = zeros(length(n.subscriptionList))
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# the more time has passed from the last time neuron was activated, the more
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@@ -680,7 +680,7 @@ function init_neuron!(id::Int64, n::linearNeuron, n_params::Dict, kfnParams::Dic
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n.synapticStrength = rand(-5:0.01:-4, length(n.subscriptionList))
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n.epsilonRec = zeros(length(n.subscriptionList))
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n.wRec = rand(-0.2:0.01:0.2, length(n.subscriptionList))
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n.wRec = randn(length(n.subscriptionList))
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n.wRecChange = zeros(length(n.subscriptionList))
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n.alpha = calculate_k(n)
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n.z_i_t_commulative = zeros(length(n.subscriptionList))
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