dev

src/learn.jl

@@ -422,7 +422,6 @@ function lifLearn(wRec,
     # transfer data to cpu
     arrayProjection4d_cpu = arrayProjection4d |> cpu
     wRec_cpu = wRec |> cpu
-    wRec_cpu = wRec_cpu[:,:,:,1]    # since every batch has the same neuron wRec, (row, col, n)
     wRecChange_cpu = wRecChange |> cpu
     wRecChange_cpu = wRecChange_cpu[:,:,:,1]
     eta_cpu = eta |> cpu
@@ -446,6 +445,7 @@ function lifLearn(wRec,
                                                         exInType_cpu,
                                                         wRecChange_cpu,
                                                         vt,
+                                                        eta,
                                                         neuronInactivityCounter_cpu,
                                                         synapseReconnectDelay_cpu,
                                                         synapticActivityCounter_cpu,
@@ -566,6 +566,8 @@ function onLearn!(wOut,
     wOut .-= 0.001 .* wOut  
 end
 
+alltrue(args...) = false ∈ [args...] ? false : true
+
 #WORKING 1) implement 90% +w, 10% -w    2) rewrite this function
 function neuroplasticity(synapseConnectionNumber, 
                         zitCumulative, # (row, col)
@@ -573,13 +575,16 @@ function neuroplasticity(synapseConnectionNumber,
                         exInType,
                         wRecChange,
                         vt,
+                        eta,
                         neuronInactivityCounter,
                         synapseReconnectDelay,
                         synapticActivityCounter,
                         progress,)  # (row, col, n) 
     i1,i2,i3 = size(wRec)
+    println("eta $(size(eta))")
+    println("wRec $(size(wRec))")
     error("DEBUG ->  neuroplasticity    $(Dates.now())")
-    #WORKING DEPEND ON modelError
+
     if progress == 2    # no need to learn
         # skip neuroplasticity
         #TODO I may need to do something with neuronInactivityCounter and other variables
@@ -588,10 +593,33 @@ function neuroplasticity(synapseConnectionNumber,
         # merge learning weight with average learning weight of all batch
         wRec .= abs.((exInType .* wRec) .+ wRecChange)  # abs because wRec doesn't carry sign
 
+        # seperate active synapse out of inactive in this signal
+        mask_inactiveSynapse = isequal.(synapticActivityCounter, 0)
+        mask_activeSynapse = (!isequal).(synapticActivityCounter, 0)
+
         # adjust weight based on vt progress and repeatition (90% +w, 10% -w) depend on epsilonRec
-        mask = isless.()
+        avgActivity = sum(synapticActivityCounter) / length(synapticActivityCounter)
+        lowerlimit = 0.1 * avgActivity
+        
+        # +w, synapse with more than 10% of avg activity get increase weight by eta
+        mask_more = (!isless).(synapticActivityCounter, lowerlimit)
+        mask_2 = alltrue.(mask_activeSynapse, mask_more)
+        mask_2 .*= 1 .+ eta  # minor activity synapse weight will be reduced by eta
+        wRec .*= mask_2
+
+        #WORKING should i -w only for activated synapse only or ALL synapse. activated synapse representing this signal e.g. number 3
+        # -w, synapse with less than 10% of avg activity get reduced weight by eta
+        mask_less = isless.(synapticActivityCounter, lowerlimit)   # 1st criteria
+        
+        mask_3 = alltrue.(mask_activeSynapse, mask_less)
+        mask_3 .*= 1 .- eta  # minor activity synapse weight will be reduced by eta
+        wRec .*= mask_3
 
         # -w all non-fire connection except mature connection
+        mask_notmature = isless.(wRec, 0.1) # 2nd criteria, weak synapse has weight < 0.1
+        mask_1 = alltrue.(mask_inactiveSynapse, mask_notmature)
+        mask_1 .*= 1 .- eta
+        wRec .*= mask_1
 
         # prune weak connection
 
@@ -810,9 +838,6 @@ end
 
 
 
-
-
-