diff --git a/src/learn.jl b/src/learn.jl
index f57e9b9..9101ab6 100644
--- a/src/learn.jl
+++ b/src/learn.jl
@@ -290,6 +290,7 @@ function learn!(kfn::kfn_1, device=cpu)
                 kfn.alif_synapticActivityCounter,
                 kfn.alif_synapseConnectionNumber,
                 kfn.alif_synapticWChangeCounter,
+                kfn.alif_eta,
                 kfn.zitCumulative,
                 device)
 
@@ -334,7 +335,6 @@ function lifLearn(wRec,
     zitCumulative_cpu = zitCumulative_cpu[:,:,1]              # (row, col)
 
     # -W if less than 10% of repeat avg, +W otherwise
-    println("wRec_cpu 1 ", wRec_cpu)
     _, _, i3 = size(wRec_cpu)
     for i in 1:i3
         x = 0.1 * (sum(synapticActivityCounter[:,:,i]) / length(synapticActivityCounter[:,:,i]))
@@ -344,12 +344,6 @@ function lifLearn(wRec,
 
     # weak / negative synaptic connection will get randomed in neuroplasticity()
     wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0)  # mark with -1.0
-    
-    # # synaptic connection that has no activity will get randomed in neuroplasticity()
-    # mask = isless.(synapticActivityCounter_cpu, -100000)
-    # GeneralUtils.replaceElements!(mask, 1, wRec_cpu, -1.0)
-    # # reset lif_inactivity elements to base value
-    # GeneralUtils.replaceElements!(mask, 1, synapticActivityCounter_cpu, 0.0)
 
     # neuroplasticity, work on CPU side
     wRec_cpu = neuroplasticity(synapseConnectionNumber,
@@ -379,6 +373,7 @@ function alifLearn(wRec,
                     synapticActivityCounter,
                     synapseConnectionNumber,
                     synapticWChangeCounter, #TODO
+                    eta,
                     zitCumulative,
                     device)
 
@@ -389,6 +384,8 @@ function alifLearn(wRec,
     arrayProjection4d_cpu = arrayProjection4d |> cpu
     wRec_cpu = wRec |> cpu
     wRec_cpu = wRec_cpu[:,:,:,1]    # since every batch has the same neuron wRec, (row, col, n)
+    eta_cpu = eta |> cpu
+    eta_cpu = eta_cpu[:,:,:,1]
     neuronInactivityCounter_cpu = neuronInactivityCounter |> cpu
     neuronInactivityCounter_cpu = neuronInactivityCounter_cpu[:,:,:,1]      # (row, col, n)
     synapticActivityCounter_cpu = synapticActivityCounter |> cpu
@@ -396,14 +393,16 @@ function alifLearn(wRec,
     zitCumulative_cpu = zitCumulative |> cpu
     zitCumulative_cpu = zitCumulative_cpu[:,:,1]              # (row, col)
 
+    # -W if less than 10% of repeat avg, +W otherwise
+    _, _, i3 = size(wRec_cpu)
+    for i in 1:i3
+        x = 0.1 * (sum(synapticActivityCounter[:,:,i]) / length(synapticActivityCounter[:,:,i]))
+        mask = GeneralUtils.replaceLessThan.(wRec_cpu[:,:,i], x, -1, 1)
+        wRec_cpu[:,:,i] .+= mask .* eta_cpu[:,:,i] .* wRec_cpu[:,:,i]
+    end
+
     # weak / negative synaptic connection will get randomed in neuroplasticity()
     wRec_cpu = GeneralUtils.replaceBetween.(wRec_cpu, 0.0, 0.01, -1.0)  # mark with -1.0
-    
-    # synaptic connection that has no activity will get randomed in neuroplasticity()
-    mask = isless.(synapticActivityCounter_cpu, -100000)
-    GeneralUtils.replaceElements!(mask, 1, wRec_cpu, -1.0)
-    # reset alif_inactivity elements to base value
-    GeneralUtils.replaceElements!(mask, 1, synapticActivityCounter_cpu, 0.0)
 
     # neuroplasticity, work on CPU side
     wRec_cpu = neuroplasticity(synapseConnectionNumber,