Implementing Multilayer Neural Network Behavior Using Polychronous Wavefront Computation

Abstract

Polychronous Wavefront Computation (PWC) is an abstraction of spiking neural networks that provides a potentially practical model for implementing neuromorphic computing systems. While it's has been shown to exhibit some basic computational capabilities, its use in complex neuro-computational models remains to be explored. The paper presents a model and approach for configuring PWC transponders to implement multilayer neural network behavior to provide a basis for more complex applications of the technology. The model uses a set of input transponders representing pattern features to stimulate hidden layer transponders that combine features and trigger output layer transponders to identify patterns. The input layer transponder geometry is selected to create wavefront intersections for all relevant feature combinations. Hidden layer transponders are positioned by solving the intersection of the circles equations defined by sets of input transponders. Output layer transponders are defined to collect complete sets of features for recognition based on the hidden layer transponder geometry. The approach uses the intersections of three wavefronts to maximize transponder selectivity and increase information density. The concept is experimentally demonstrated and analyzed with a 7-segment display digit recognition application which provides a simple but representative example of more complex pattern recognition problems.