diff --git a/src/learn.jl b/src/learn.jl
index 32bc42a..8cda437 100644
--- a/src/learn.jl
+++ b/src/learn.jl
@@ -23,40 +23,40 @@ end
 """ knowledgeFn learn()
 """
 function learn!(kfn::kfn_1, correctAnswer::BitVector)  
-    # # 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
+    # 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
 
-                learn!(kfn.outputNeuronsArray[i], kfnError)     
-            else    # output neuron that is NOT associated with correctAnswer
-                learn!(kfn.outputNeuronsArray[i], kfnError)
+            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
+
+    #             learn!(kfn.outputNeuronsArray[i], kfnError)     
+    #         else    # output neuron that is NOT associated with correctAnswer
+    #             learn!(kfn.outputNeuronsArray[i], kfnError)
+    #         end
+    #     end
+    # end
     
     # wrap up learning session
     if kfn.learningStage == "end_learning"