Unsupervised Learning of Patterns Using Multilayer Reverberating Configurations of Polychronous Wavefront Computation

Abstract

Polychronous Wavefront Computation (PWC) is an abstraction of spiking neural networks that has been shown to be capable of basic computational functions and simple pattern recognition through multilayer configurations. The objective of this work is to apply unsupervised learning methods to multilayer PWC configurations to improve performance providing a basis for more advanced applications and deep learning. Previous work on defining multilayer PWC configurations is extended by applying biologically inspired learning methods to dynamically suppress unneeded transponders and improve configuration performance. Simple learning approaches based on concepts from spike-timing-dependent plasticity and potentiation decay models are adapted to PWC transponders and combined with training sequences to optimize the transponder configurations for recognition. Learning is further enhanced by configuring transponders in recurrent structures to activate hidden layer transponders creating reverberations that reinforce learning. A means to classify multiple input patterns into general concepts is also introduced to further enhance the recognition capabilities of the configurations. The concepts are experimentally validated and analyzed through application to a 7-segment display digit recognition problem showing that the approach can improve PWC configuration performance and reduce complexity.