Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation

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 spi...

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Bibliographic Details
Published in:Frontiers in neuroscience 2021-02, Vol.15, p.627221-627221
Main Authors: Hazan, Avi, Ezra Tsur, Elishai
Format: Article
Language:English
Subjects:
Artificial intelligence
brain-inspired electronics
Energy efficiency
Field programmable gate arrays
Firing pattern
neural engineering framework
Neural networks
neuromorphic electronics
neuromorphic engineering
Neurons
Neuroscience
Robotics
spiking neural networks
Transistors
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Summary: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.
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2021.627221