R. Ritz and J. L. van Hemmen.
Pattern segmentation and feature linking as simultaneous processes in an associative network of spiking neurons.
In: S. Gielen and B. Kappen (eds.) ICANN '93, Proceedings of the International Conference on Artificial Neural Networks, pages 914-917, Springer, Berlin Heidelberg New York, 1993.

Abstract

Feature linking and segmentation of four stationary patterns are shown to be performed as simultaneous processes by a fully connected, auto-associative neural network of spiking neurons. The patterns have been learned through an asymmetric, Hebbian rule that can handle a varying low activity. In this case the total activity of the patterns ranges between 4 and 7%. The underlying model is the `spike response model'. Spiking is achieved by an absolute refractory period (1ms) while an inhibitory delay loop prevents continuous firing. Reaching the synapse after some axonal delay each spike evokes an excitatory or inhibitory postsynaptic potential (EPSP or IPSP) with a realistic response at the receiving neuron. Each neuron sums up its input signals linearly and acts as a noisy threshold element for generating a new spike.