A Macrocolumn Architecture Implemented with Spiking Neurons
Abstract
The macrocolumn is a key component of a neuromorphic computing system that interacts with an external environment under control of an agent. Environments are learned and stored in the macrocolumn as labeled directed graphs where edges connect features and labels indicate the relative displacements between them. Macrocolumn functionality is first defined with a state machine model. This model is then implemented with a neural network composed of spiking neurons. The neuron model employs active dendrites and mirrors the Hawkins/Numenta neuron model. The architecture is demonstrated with a research benchmark in which an agent employs a macrocolumn to first learn and then navigate 2-d environments containing pseudo-randomly placed features.
Cite
@article{arxiv.2207.05081,
title = {A Macrocolumn Architecture Implemented with Spiking Neurons},
author = {James E. Smith},
journal= {arXiv preprint arXiv:2207.05081},
year = {2023}
}
Comments
This is a major revision. Neuron outputs are encoded as the body potential. Winner-take-all inhibition then compares body potentials to determine a winner. At the end of each cycle, a non-zero WTA output is converted to a binary spike. This method remains consistent with temporal neuron operation internal to a cycle, with only a single bit of temporal precision being maintained between cycles