experimenting compute neuron association
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@@ -14,11 +14,11 @@ function (m::model)(input_data::AbstractVector)
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m.timeStep += 1
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# process all corresponding KFN
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raw_model_respond, outputNeuron_v_t1 = m.knowledgeFn[:I](m, input_data)
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# raw_model_respond, outputNeuron_v_t1, firedNeurons_t1 = m.knowledgeFn[:I](m, input_data)
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# the 2nd return (KFN error) should not be used as model error but I use it because there is
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# only one KFN in a model right now
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return raw_model_respond::Array{Bool}, outputNeuron_v_t1::Array{Float64}
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return m.knowledgeFn[:I](m, input_data)
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end
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#------------------------------------------------------------------------------------------------100
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@@ -96,7 +96,8 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
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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}
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return out::Array{Bool}, outputNeuron_v_t1::Array{Float64}, sum(kfn.firedNeurons_t1),
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kfn.exSignalSum, kfn.inSignalsum
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end
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#------------------------------------------------------------------------------------------------100
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@@ -220,7 +221,18 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
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n.v_t1 = n.alpha * n.v_t
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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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n.recSignal = sum(n.wRec .* n.z_i_t) # signal from other neuron that this neuron subscribed
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recSignal = n.wRec .* n.z_i_t
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if n.id == 1 #FIXME debugging output neuron dead
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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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end
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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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n.v_t1 = no_negative!(n.v_t1)
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