refractoring

src/snn_utils.jl

@@ -244,31 +244,6 @@ end
 firing_rate!(n::compute_neuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
 firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarget
 
-function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
-    # if there is 0-weight then replace it with new connection
-    zero_weight_index = findall(iszero.(n.wRec))
-    if length(zero_weight_index) != 0
-        """ sampling new connection from list of neurons that fires instead of ramdom choose from
-        all compute neuron because there is no point to connect to neuron that not fires i.e.
-        not fire = no information
-        """
-
-        subscribe_options = filter(x -> x ∉ [n.id], firedNeurons)  # exclude this neuron id from the list
-        filter!(x -> x ∉ n.subscriptionList, subscribe_options) # exclude this neuron's subscriptionList from the list
-        shuffle!(subscribe_options)
-    end
-
-    new_connection_percent = 10 - ((n.optimiser.eta / 0.0001) / 10) # percent is in range 0.1 to 10
-    percentage = [new_connection_percent, 100.0 - new_connection_percent] / 100.0
-    for i in zero_weight_index
-        if Utils.random_choices([true, false], percentage)
-            n.subscriptionList[i] = pop!(subscribe_options)
-            n.wRec[i] = 0.01 # new connection should not send large signal otherwise it would throw 
-                                # RSNN off path. Let weight grow by an optimiser
-        end
-    end
-end
-
 function adjust_internal_learning_rate!(n::compute_neuron)
     n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 :
                                 n.internal_learning_rate * 1.005
@@ -298,9 +273,11 @@ function synapticConnStrength(currentStrength::AbstractFloat, updown::String, bi
     return updatedStrength
 end
 
+""" Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
+    below lowerlimit.
+"""
 function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
     for (i, connStrength) in enumerate(n.synapticStrength)
-        #WORKING
         # check whether connStrength increase or decrease based on usage from n.epsilonRec
         updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
         updatedConnStrength = synapticConnStrength(connStrength, updown)
@@ -312,8 +289,53 @@ function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
         end
     end
 end
+
 function synapticConnStrength!(n::input_neuron) end
 
+function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
+    # if there is 0-weight then replace it with new connection
+    zero_weight_index = findall(iszero.(n.wRec))
+    if length(zero_weight_index) != 0
+        """ sampling new connection from list of neurons that fires instead of ramdom choose from
+        all compute neuron because there is no point to connect to neuron that not fires i.e.
+        not fire = no information
+        """
+
+        subscribe_options = filter(x -> x ∉ [n.id], firedNeurons)  # exclude this neuron id from the list
+        filter!(x -> x ∉ n.subscriptionList, subscribe_options) # exclude this neuron's subscriptionList from the list
+        shuffle!(subscribe_options)
+    end
+
+    new_connection_percent = 10 - ((n.optimiser.eta / 0.0001) / 10) # percent is in range 0.1 to 10
+    percentage = [new_connection_percent, 100.0 - new_connection_percent] / 100.0
+    for i in zero_weight_index
+        if Utils.random_choices([true, false], percentage)
+            n.subscriptionList[i] = pop!(subscribe_options)
+            n.wRec[i] = 0.01 # new connection should not send large signal otherwise it would throw 
+                                # RSNN off path. Let weight grow by an optimiser
+        end
+    end
+end
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