I have reached some limits using Python, Tkinter and Matplotlib and I had to switch to DearPyGUI and converting more code into C through Cython. This took awhile but results are good for now, I did not realized how slow was Matplotlib with plot drawing..
Now that I have more tools to see what’s going on, I realized that LTD/P effects are more complex than I envisioned.. if a neuron responds faster to an input signal (say because of an LTP effect, or higher frequency input signal or because it has more connections or because is less inhibited ) then it immediately alters the flow of information … Weak synapses become oriented resulting in (as of now) unpredictable results.
Inhibition is also more complex, a neuron under inhibition could be forced to wait for the second activation signal to become activated, but I’m still not sure of how to do it… Right now the neuron remains in an undefined state when inhibited… Is not firing but is not in re-pause either, I haven’t decided what to do with it..
With more speed I could let the synapses form and break indefinitely and it became clear that there are strong synapses defined by data flow and weak synapses that form and break immediately, about 10% are weak synapses..