A neural network model of temporal code generation and position-invariant pattern recognition

Abstract

Numerous studies have suggested that the brain may encode information in the temporal firing pattern of neurons. However, little is known regarding how information may come to be temporally encoded and about the potential computational advantages of temporal coding. Here, it is shown that local inhibition may underlie the temporal encoding of spatial images. As a result of inhibition, the response of a given cell can be significantly modulated by stimulus features outside its own receptive field. Feedforward and lateral inhibition can modulate both the firing rate and temporal features, such as latency. In this article, it is shown that a simple neural network model can use local inhibition to generate temporal codes of handwritten numbers. The temporal encoding of a spatial pattern has the interesting and computationally beneficial feature of exhibiting position invariance. This work demonstrates a manner by which the nervous system may generate temporal codes and shows that temporal encoding can be used to create position-invariant codes.