Implementation of Pulse-Coupled Neural Networks in a CNAPS Environment

INTRODUCTION There are several advantageous with analogue implementations of neural networks. The main one is, of course, that most signal sensors generate analogue signals. Several years ago Intel introduced the Electrically Trainable Neural Network, ETANN i80170NX [1-4], with a fairly conventional back-propagation (BP) algorithm and a processing time of a few micro seconds. The main disadvantageous of the analogue implementations are the relatively low precision (12 %), the sensitivity to noise and the fact that one generally requires some post-processing of the outputs from the neural networks. This is generally performed using some von Neumann computer, i.e a digital machine. Pulse Coupled neural Networks, PCNN, [5-10] could easily be implemented as hybrid circuits, i.e. an analogue input and a digital output. One would simply have an analogue input to the circuit and make use of the inherent feature that the output is a temporal series of binary images. However, as has be