This paper explains how location signals can be generated with a location layer that utilizes grid-cell-like neurons. It builds on our previous paper, A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.
The paper walks through our implementation of brain-like SDRs in practical systems as a proof of concept. We implemented a sparse layer that can be dropped into existing deep learning and convolutional networks. We then trained sparse networks with back propagation, validated them with popular datasets and tested their accuracy with noisy images and sounds.
In this research paper, Numenta proposes a novel theoretical framework for understanding what the neocortex does and how it does it. The framework is based on grid cells and has significant implications for neuroscience and machine intelligence.
This companion piece explains the Thousand Brains Theory of Intelligence, one of the big ideas introduced in the October 2018 research paper A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex by Jeff Hawkins, Marcus Lewis, Scott Purdy, Mirko Klukas, and Subutai Ahmad. Written by non-neuroscientists, it can be read as a standalone piece or as a primer for the full research paper.
This paper describes an important component of HTM, the HTM spatial pooler, which is a neurally inspired algorithm that learns sparse distributed representations online. Written from a neuroscience perspective, the paper demonstrates key computational properties of the HTM spatial pooler.
