train compute neuron with associated output neuron

src/Ironpen.jl

@@ -34,10 +34,9 @@ using .interface
 
 """ 
     Todo:
-        [1] implement connection strength based on right or wrong answer
-        [2] during 0 training if 1-9 output neuron fires, adjust weight only those neurons
-        [3] implement dormant connection
-        [4] Δweight * connection strength 
+        [2] implement connection strength based on right or wrong answer
+        [4] implement dormant connection
+        [3] Δweight * connection strength 
         [] using RL to control learning signal
         [] consider using Dates.now() instead of timestamp because time_stamp may overflow
         [5] training should include adjusting α, neuron membrane potential decay factor
@@ -62,6 +61,7 @@ using .interface
         [DONE] wRec should not normalized whole. it should be local 5 conn normalized.
         [DONE] neuroplasticity() i.e. change connection
         [DONE] add multi threads
+        [DONE] during 0 training if 1-9 output neuron fires, adjust weight only those neurons
 
     Change from version:    v06_36a
         - 

src/learn.jl

@@ -23,19 +23,38 @@ end
 """ knowledgeFn learn()
 """
 function learn!(kfn::kfn_1, correctAnswer::BitVector)  
-    # compute kfn error
+    # # compute kfn error for each neuron
+    # outs = [n.z_t1 for n in kfn.outputNeuronsArray]
+    # for (i, out) in enumerate(outs)
+    #     if out != correctAnswer[i]   # need to adjust weight
+    #         kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) * 
+    #                     100 / kfn.outputNeuronsArray[i].v_th )
+
+    #         Threads.@threads for n in kfn.neuronsArray
+    #         # for n in kfn.neuronsArray
+    #             learn!(n, kfnError)
+    #         end
+
+    #         learn!(kfn.outputNeuronsArray[i], kfnError)            
+    #     end
+    # end
+
+    #TESTING compute kfn error for each neuron
     outs = [n.z_t1 for n in kfn.outputNeuronsArray]
     for (i, out) in enumerate(outs)
         if out != correctAnswer[i]   # need to adjust weight
             kfnError = ( (kfn.outputNeuronsArray[i].v_th - kfn.outputNeuronsArray[i].vError) * 
                         100 / kfn.outputNeuronsArray[i].v_th )
+            if correctAnswer[i] == 1    # output neuron that associated with correctAnswer
+                Threads.@threads for n in kfn.neuronsArray
+                # for n in kfn.neuronsArray
+                    learn!(n, kfnError)
+                end
 
-            Threads.@threads for n in kfn.neuronsArray
-            # for n in kfn.neuronsArray
-                learn!(n, kfnError)
+                learn!(kfn.outputNeuronsArray[i], kfnError)     
+            else    # output neuron that is NOT associated with correctAnswer
+                learn!(kfn.outputNeuronsArray[i], kfnError)
             end
-
-            learn!(kfn.outputNeuronsArray[i], kfnError)            
         end
     end