A continuous input RAM-based stochastic neural model

Abstract

An extension of the probabilistic random access memory (pRAM) neural model is presented, which is shown to have a natural capacity for generalisation. This is displayed in one-and two-dimensional spatial learning tasks, using a form of reinforcement training. The model is then further extended to allow for the learning of temporal sequences, and this capacity is demonstrated in a simple temporal learning problem.