Introduction

Neural networks with plasticity (dynamic connection coefficients) can recognize and associate stimulus patterns. Previous studies have shown the usefulness of a simple algorithm called ‘brain-washing’ which leads to networks which can have many eligible neurons with large variations in activity and complex cyclic modes (Clark & Winston, 1984). Methods of modifying connection coefficients are discussed and evaluated.

Successful pattern recognition with quasirandom, rather than topographic, networks would be much more significant and general. There is no doubt that topographic networks could be more efficient in the brain but less adaptable to changing conditions. A quasirandom network could be trained to recognize temporal and spatial stimuli, while a topographic network would be limited to a particular type of stimuli.

Three types of neurons have been incorporated into the network. A group of 10 input (stimulus) neurons (Ni) send µi efferents to neurons in the main network (see Figs. 30.1 and 30.2). Neurons in the main network are interconnected by µa afferent connections and µe efferent connections. In addition a group of output neurons (No) can be included to monitor activity of the main network and to train the network.

Components of a successful, sensible and biologically feasible training algorithm will be discussed.

Physical limitations to training algorithms

A specified neuron obtains the majority of information from afferent and efferent neurons.