Cortically-Inspired Overcomplete Feature Learning for Colour Images

Abstract

The Hierarchical Temporal Memory (HTM) framework is a deep learning system inspired by the functioning of the human neocortex. In this paper we investigate the feasibility of this framework by evaluating the performance of one component, the spatial pooler. Using a recently developed implementation, the augmented spatial pooler (ASP), as a single layer feature detector, we test its performance using a standard image classification pipeline. The main contributions of the paper are the implementation and evaluation of modifications to ASP that enable it to form overcomplete representations of the input and to form connections with multiple data channels. Our results show that these modifications significantly improve the utility of ASP, making its performance competitive with more traditional feature detectors such as sparse restricted Boltzmann machines and sparse auto-encoders.