I thought this to be straight forward, but I’ve been stuck on this issue for a long time now. Assume you have a single synapse and add a second one with exactly the same properties. The signal doubles and the time to neuronal activation is cut in half (something that I don’t believe it to be true). I’ll refer to the time since receiving the first activation signal till neuronal activation as: cycles to activation or for short cta. Changing one of the synapses somehow will result in complications. Assume synapse S1 by itself will fire the neuron at a cta = x. Altering the synapse will result in a cta = y. Then when combining synapse S1 with cta = y with a synapse S2 with cta = x, what should be the resulting cta ? That is not clear at all. It can’t be an average time, because the weak synapses will have too much sway over the stronger synapses. My current model, dealt with this in a precise theoretical manner. But it fails when synapses are not equal. The adapting mechanism eliminates the weak synapse. I put in some conditions that would stop the process of elimination and apparently was working well, but was not 100% consistent. That did not bother me that much until I started doing more complex simulations.. Then it mattered. Not having consistency in latency, resulted in poor outcomes. After much time spent, I concluded that there is no theoretical way of establishing what should the result be when combining cta = x with cta = y. The biological neuron seems to settle this debate through some constants, there are various ratios between Na + and Ca2+ entering the membrane of the post-synaptic neuron, number of AMPA / NMDA receptors, the NMDA receptor is slower than the AMPA receptor. All this (and others) lead me to believe that there cannot be a beautiful kinetic model that would rule everything. The combination of different cta’s most likely is not precise (as in [Ca2+] is NOT linearly correlated with the [AMPA receptor] ) but must be consistent, which is what’s important.
TECHNOLOGIES