Computing with a difference neuron

Abstract

The need for efficient implementation of neural networks in silicon is used to motivate the investigation of alternatives to the McCulloch-Pitts neuron; in particular, one which computes the norm of a difference rather than an inner product. Earlier work is reviewed briefly and formal relationships between the two types are provided. The two types are shown to be equivalent under certain circumstances. Hopfield-like networks and multilayer feedforward networks of the difference neuron are simulated and analysed by comparing them to conventional types. Outside the domain of equivalence, the difference networks are found to perform less well than networks of identical architecture but which incorporate the McCulloch-Pitts neuron.