add todo

src/Ironpen.jl

@@ -35,9 +35,10 @@ using .interface
 """ 
     Todo:
         [*3] no "start learning" use reset learning and "inference", "learning" mode instead
-        [4] add time-based learning method. Also implement "thinking period"
-        [5] verify that model can complete learning cycle with no error
-        [6] neuroplasticity() with synaptic connection strength concept
+        [6] add time-based learning method. Also implement "thinking period"
+        [7] verify that model can complete learning cycle with no error
+        [4] synaptic connection strength concept
+        [5] neuroplasticity() i.e. change connection
         [] using RL to control learning signal
         [] consider using Dates.now() instead of timestamp because time_stamp may overflow
         [] training should include adjusting α, neuron membrane potential decay factor

src/forward.jl

@@ -126,6 +126,7 @@ function (n::lif_neuron)(kfn::knowledgeFn)
     
         n.alpha_v_t = n.alpha * n.v_t
         n.v_t1 = n.alpha_v_t + n.recurrent_signal
+        n.v_t1 = no_negative!.(n.v_t1)
 
         if n.v_t1 > n.v_th
             n.z_t1 = true
@@ -169,6 +170,7 @@ function (n::alif_neuron)(kfn::knowledgeFn)
         n.recurrent_signal = sum(n.w_rec .* n.z_i_t)  # signal from other neuron that this neuron subscribed
         n.alpha_v_t = n.alpha * n.v_t
         n.v_t1 = n.alpha_v_t + n.recurrent_signal
+        n.v_t1 = no_negative!.(n.v_t1)
         if n.v_t1 > n.av_th
             n.z_t1 = true
             n.refractory_counter = n.refractory_duration

src/learn.jl

@@ -12,14 +12,38 @@ export learn!
 
 function learn!(m::model, model_respond, correct_answer)
     if m.learning_stage == "learning"
+        #WORKING compute error
+        if m.time_stamp < m.model_params[:perfect_timing]
+            too_early = m.model_params[:perfect_timing] - m.time_stamp
+            model_error = (model_respond .- correct_answer) * too_early
+
+
+
+
+
+
+
+
+
+
+
+
         model_error = Flux.logitcrossentropy(model_respond, correct_answer)
         output_elements_error = model_respond - correct_answer
 
         learn!(m.knowledgeFn[:I], model_error, output_elements_error)
 
-        #WORKING compute error
-        # if m.time_stamp < m.m
-        model_error = model_respond .- correct_answer
+
+
+        
+        
+
+
+
+
+
+
+
         
 
 
@@ -34,18 +58,18 @@ function learn!(m::model, model_respond, correct_answer)
 end
 
 
-function learn!(m::model, raw_model_respond, correct_answer=nothing)
-    if m.learning_stage != "doing_inference"
-        model_error = Flux.logitcrossentropy(raw_model_respond, correct_answer)
-        output_elements_error = raw_model_respond - correct_answer
+# function learn!(m::model, raw_model_respond, correct_answer=nothing)
+#     if m.learning_stage != "doing_inference"
+#         model_error = Flux.logitcrossentropy(raw_model_respond, correct_answer)
+#         output_elements_error = raw_model_respond - correct_answer
 
-        learn!(m.knowledgeFn[:I], model_error, output_elements_error)
-    else
-        model_error = nothing
-    end
+#         learn!(m.knowledgeFn[:I], model_error, output_elements_error)
+#     else
+#         model_error = nothing
+#     end
 
-    return model_error
-end
+#     return model_error
+# end
 
 
 

src/snn_utils.jl

@@ -1,7 +1,7 @@
 module snn_utils
 
 using Flux.Optimise: apply!
-export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron, no_negative,
+export calculate_α, calculate_ρ, calculate_k, timestep_forward!, init_neuron, no_negative!,
         precision, calculate_w_change!, store_knowledgefn_error!, interneurons_adjustment!, 
         reset_z_t!, reset_learning_params!, reset_learning_history_params!,
         cal_v_reg!, calculate_w_change_end!,
@@ -28,7 +28,7 @@ function timestep_forward!(x::linear_neuron)
     x.out_t = x.out_t1
 end
 
-no_negative(n) = n < 0.0 ? 0.0 : x 
+no_negative!(x) = x < 0.0 ? 0.0 : x 
 precision(x::Array{<:Array}) = ( std(mean.(x)) / mean(mean.(x)) ) * 100
 
 # reset functions for LIF/ALIF neuron