add neuroplasticity for output neuron

src/forward.jl

@@ -60,7 +60,7 @@ function (kfn::kfn_1)(m::model, input_data::AbstractVector)
                 for i in 1:length(input_data)]
     # noise = [rand(rng, Distributions.Binomial(1, 0.5)) for i in 1:10]     # another option
 
-    input_data = [noise; input_data]  # noise start from neuron id 1
+    input_data = [noise; input_data]  # noise must start from neuron id 1
 
     for n in kfn.neuronsArray
         timestep_forward!(n)
@@ -218,13 +218,13 @@ function (n::linearNeuron)(kfn::T) where T<:knowledgeFn
     
         # decay of v_t1
         n.v_t1 = n.alpha * n.v_t
-        n.vError = n.v_t1
+        n.vError = n.v_t1   # store voltage that will be used to calculate error later
     else
         n.recSignal = sum(n.wRec .* n.z_i_t)  # signal from other neuron that this neuron subscribed
         n.alpha_v_t = n.alpha * n.v_t
         n.v_t1 = n.alpha_v_t + n.recSignal
         n.v_t1 = no_negative!(n.v_t1)
-        n.vError = n.v_t1
+        n.vError = n.v_t1   # store voltage that will be used to calculate error later
         if n.v_t1 > n.v_th
             n.z_t1 = true
             n.refractoryCounter = n.refractoryDuration