start version 0.0.6

src/Ironpen.jl

@@ -32,16 +32,38 @@ using .learn
 # using .interface
 #------------------------------------------------------------------------------------------------100
 
-""" version 0.0.5
+""" version 0.0.6
     Todo:
-        [4] implement dormant connection
+        [] implement dormant connection
         [] 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
+        [] training should include adjusting α, neuron membrane potential decay factor
             which defined by neuron.tau_m formula in type.jl
 
 
-    Change from version:    0.0.4
+    Change from version:    0.0.5
+        -
+
+    
+    All features
+        - synapticStrength apply at the end of learning
+        - collect ΔwRecChange during online learning (0-784th) and merge with wRec at 
+            the end learning (800th).
+        - multidispatch + for loop as main compute method
+        - allow -w_rec                      yes
+        - voltage drop when neuron fires    voltage drop equals to vRest
+        - v_t decay during refractory
+        - input data                        population encoding, each pixel data => 
+                                                population encoding, ralative between pixel data
+        - compute neuron weight init        rand()
+        - output neuron weight init         randn()
+        - compute pseudo derivative (n.phi) every time step
+        - add excitatory, inhabitory to neuron
+        - implement "start learning", reset learning and "learning", "end_learning and
+            "inference"
+        - synaptic connection strength concept. use sigmoid, turn connection offline
+        - neuroplasticity() i.e. change connection
+        - add multi threads
         - compute model error in main loop so one could decide when to calculate error in 
             training sequence and how to calculate
         - fix ALIF adaptation formula, now n.a compute avery time step
@@ -65,28 +87,6 @@ using .learn
             on the correct answer -> strengthen the right neural pathway (connections) -> 
             this correct neural pathway resist to change. 
             Not used connection should dissapear (forgetting).
-
-    
-    All features
-        - synapticStrength apply at the end of learning
-        - collect ΔwRecChange during online learning (0-784th) and merge with wRec at 
-            the end learning (800th).
-        - multidispatch + for loop as main compute method
-        - allow -w_rec                      yes
-        - voltage drop when neuron fires    voltage drop equals to vRest
-        - v_t decay during refractory
-        - input data                        population encoding, each pixel data => 
-                                                population encoding, ralative between pixel data
-        - compute neuron weight init        rand()
-        - output neuron weight init         randn()
-        
-        - compute pseudo derivative (n.phi) every time step
-        - add excitatory, inhabitory to neuron
-        - implement "start learning", reset learning and "learning", "end_learning and
-            "inference"
-        - synaptic connection strength concept. use sigmoid, turn connection offline
-        - neuroplasticity() i.e. change connection
-        - add multi threads
         
 
     Removed features