This seems like a simple question for a ML algorithm .. train some variables to some values and the combination uniquely identifies something. But those variables can also change when something new is learned and you lose the initial meaning..
From my previous post, I concluded that learning must be somewhat random, but I was not happy with that conclusion. After all we can all tell a circle is a circle, so it can’t be that random. So I eventually came up with a middle ground theory.. Assume 4 patterns with equal probability, there has to be a network configuration that would be stable and have all 4 patterns “memorized” as long as no additional information (additional patterns) are entering the network. I managed to prove there are such states in a 3 then 4 neurons matrix. But in a 4 neuron matrix there is more than one such stable configuration and also there are also states that are stable but not specific, meaning a neuron will learn 2 patterns and another one will learn the remaining 2 patterns in a symmetrical configuration. That was to be expected but still depressing 🙁 . So the new theory is simplistic and incomplete, but I’m sure is the basis for “learning”. Still have to find a way to make learning more stable (perhaps permanent through additional synaptic variable).
I’ve also started treating the neuron more like an atom with electrons, the electrons being the synapses. So I’m actually moving away from the “fitting” hypothesis (and synaptic strength). This theory lead me to believe that there must be “empty” places on a dendrite where there is no synapse, but a synapse could have been there, a forbidden energetic location, used to separate related patterns. So all synapses have defined energies that can be perturbed by input data, but perturbation will still lead to a defined configuration or will just get back to initial state with no change. I just don’t see how a synapse could be a continuous function.
In conclusion I’m still far far away from any meaningful progress 🙁