I have programmed in the first step, linking synapses on multiple directional dendrite, but no branching yet. However, when running this model I discovered that my kinetics for synapse potential, don’t work well, they were good enough for previous model, but in essence a kluge job that worked for the wrong reasons. Basically I’m not converging well to the firing potential of the neuron, I’m overshooting and the correction, which is not good either, messes up the timing of the firing event. The result is devious and cascades into the following layers resulting eventually into a wrong learning pattern. I added ordinary differential equations, but it did not help, the problem comes from, dP/dC, where P = potential and C is the cycle number … The cycle number is an integer and I can’t do anything about it => the convergence is still poor => W (AMPA receptors) fluctuates from pattern to pattern => a delay in forming a stable pattern in the next layer => that patterns is completely inhibited if it competes with another pattern
I don’t want to reproduce real biological data in my simulation, but when I get stuck I look for inspiration in real data :). For now I’m only interested in the upward trend but I still wonder why the downward side looks so …. not symmetric .. Why does it take so long to go back to the initial state ? How long does it take though ? Can a neighboring neuron fire twice in the amount of time it takes for this synapse to regenerate ? Can this neuron fire again while this particular synapse is regenerating ?
I extracted many equations from the code hoping to solve them mathematically… but no luck there either, I don’t know how to solve so many linked simple equations.. but maybe someone does ..