C. D. Schuman, T. E. Potok, S. Young, R. Patton, G. Perdue, G. Chakma, A. Wyer, G. S. Rose

July, 2017

Neuromorphic Computing Symposium

https://dl.acm.org/citation.cfm?id=3183612

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Abstract

In this work, we apply a spiking neural network model and an associated memristive neuromorphic implementation to an application in classifying temporal scientific data. We demonstrate that the spiking neural network model achieves comparable results to a previously reported convolutional neural network model, with significantly fewer neurons and synapses required.

Citation Information

Text

author          C. D. Schuman and T. E. Potok and S. Young and R. Patton and G. Perdue and
                G. Chakma and A. Wyer and G. S. Rose
title           Neuromorphic computing for temporal scientific data classification
booktitle       Neuromorphic Computing Symposium
month           July
year            2017
doi             10.1145/3183584.3183612
url             https://dl.acm.org/citation.cfm?id=3183612

Bibtex

@INPROCEEDINGS{spy:17:nct,
    author = "C. D. Schuman and T. E. Potok and S. Young and R. Patton and G. Perdue and
                G. Chakma and A. Wyer and G. S. Rose",
    title = "Neuromorphic computing for temporal scientific data classification",
    booktitle = "Neuromorphic Computing Symposium",
    month = "July",
    year = "2017",
    doi = "10.1145/3183584.3183612",
    url = "https://dl.acm.org/citation.cfm?id=3183612"
}