Unsupervised Learning of Polychronous Wavefront Computation Configurations for Pattern Recognition

Abstract

Polychronous Wavefront Computation (PWC) provides a potentially simple model for large scale implementation of spiking neural networks and deep learning. Although the definition of predefined pattern recognition configurations has been demonstrated, dynamic organization of configurations from examples remains a difficult problem. This paper explores the hypothesis that a properly arranged field of PWC transponders with neuromorphic behaviors can self-organize into recognition configurations based on training examples. The PWC transponders used are augmented with a position learning algorithm based on spike-timing-dependent plasticity, suppression of non-specific transponders using a stimulation fatigue approach and deactivation of unused transponders using potentiation decay. The paper provides the results of initial research demonstrating that pattern recognition configurations can be learned if the initial density and distribution of transponders is properly selected with respect to the learning behavior parameters. The effectiveness of the learning process can be improved by encoding layering information in the wavefronts to focus the transponder activations. The results define a means for PWC transponders to self-organize into recognition configurations providing a basis for development of more complex configurations and deep learning applications.