Local Synaptic Rules with Maximal Information Storage Capacity
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TL;DR
A family of mathematical neural network models that investigate learning and synaptic plasticity that is a long way from the reality of magnetic materials, but still further remote from real brain dynamics.
Abstract
A mathematical model of a natural process is always a kind of caricature. Today the physicists' most popular model for the process of neural activation in the brain is the so-called spin-glass model — a mathematical model that is derived from theoretical investigations on Ising spins and has a certain formal similarity to mathematical models of brain dynamics. This type of model is certainly a long way from the reality of magnetic materials, but it is still further remote from real brain dynamics. On the other hand, it has often proved to be beneficial for a theoretical insight to step back from too close a look at reality and to work with caricatures. It is in this spirit that we want to compare and analyse a family of mathematical neural network models that investigate learning and synaptic plasticity (e.g. [1],[3],[4]).