… and it did not work… I spent close to a month on this and nothing. And this was supposed to be easy. The idea was to have neurons “learn” to respond to certain frequencies and perhaps not to other. Something like this:
It did not work within the model I should say. Because otherwise is just training a variable to a certain value and it’s done. But within my model, I could not find a variable that would fit the purpose and that’s because no variable is truly irreversible. So the two neurons N2 and N3, initially learn pattern 1 respectively pattern 2 but then after couple of trials switching between patterns, both learn just on pattern and do not respond to the other one and if I insist with the pattern with no response then neurons will adapt and respond again to that pattern. Then I started doubting the whole idea of “learning” a certain frequency. Is the learning process for frequency at synapse level? At neuron (body) level ? Add to this the fact that Firing Rates are either faster or slower right at the beginning of a new pattern :
and there you have it, doubting everything.. I read online to see how the biological neuron adapts to changing frequencies but I found nothing of interest.. vague explanations about Potassium Channels and vague descriptions of “natural” frequencies of a neuron. So I’m giving up on the 1 Neuron applications. With a single neuron I can’t really determine the impact of learning frequencies a certain way .. Also lately I started doubting the “synaptic strength ” interpretation. While that process is real (increase of AMPA receptors in the postsynaptic neuron), I don’t think it plays a role in the learning process. It may play an indirect role such as breaking or forming new synapses but by itself is not “learning”.
What’s next ? I’m going to switch to a 2 by 2 model, see if I can learn more from that.