C. D. Schuman, H. Das, J. S. Plank, A. Aziz and G. S. Rose

October, 2023

57th Asilomar Conference on Signals, Systems, and Computers

https://ieeexplore.ieee.org/document/10477027

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Abstract

A key issue in the field of neuromorphic computing is how much inspiration to take from the brain in the im-plementation of neuromorphic hardware systems. For example, neuron implementations in neuromorphic hardware have varied from non-spiking McCulloch-Pitts style neurons to extremely biologically-detailed Hodgkin Huxley style neurons. In this work, we examine a variety of biologically-inspired features to include in neuron models for neuromorphic systems. We evaluate features such as leak, absolute refractory period, and relative refractory period. We evaluate these features in terms of their impact on algorithm and application performance.

Citation Information

Text

author      C. D. Schuman and H. Das and J. S. Plank and A. Aziz and G. S. Rose
title       Evaluating Neuron Models through Application-Hardware Co-Design
booktitle   57th Asilomar Conference on Signals, Systems, and Computers
year        2023
month       October
doi         10.1109/IEEECONF59524.2023.10477027
url         https://ieeexplore.ieee.org/document/10477027

Bibtex

@INPROCEEDINGS{sdp:23:enm,
    author = "C. D. Schuman and H. Das and J. S. Plank and A. Aziz and G. S. Rose",
    title = "Evaluating Neuron Models through Application-Hardware Co-Design",
    booktitle = "57th Asilomar Conference on Signals, Systems, and Computers",
    year = "2023",
    month = "October",
    doi = "10.1109/IEEECONF59524.2023.10477027",
    url = "https://ieeexplore.ieee.org/document/10477027"
}