Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.
Bibliographical noteFunding Information:
The authors would like to thank Tamara Perelman Tsur for her insightful comments. Funding. This research was supported by the Israel Innovation Authority (EzerTech) and the Open University of Israel research grant.
© Copyright © 2021 Hazan and Ezra Tsur.
- brain-inspired electronics
- neural engineering framework
- neuromorphic electronics
- neuromorphic engineering
- spiking neural networks