A general model of stochastic neural processing

Abstract

A model of neural processing is proposed which is able to incorporate a great deal of neurophysiological detail, including effects associated with the mechanics of postsynaptic summation and cell surface geometry and is capable of hardware realisation as a `probabilistic random access memory' (pRAM). The model is an extension of earlier work by the authors, which by operating at much shorter time scales (of the order of the lifetime of a quantum of neurotransmitter in the synaptic cleft) allows a greater amount of information to be retrieved from the simulated spike train. The mathematical framework for the model appears to be that of an extended Markov process (involving the firing histories of the N neurons); simulation work has yielded results in excellent agreement with theoretical predictions. The extended neural model is expected to be particularly applicable in situations where timing constraints are of special importance (such as the auditory cortex) or where firing thresholds are high, as in the case for the granule and pyramidal cells of the hippocampus.