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2017 | OriginalPaper | Buchkapitel

Computing with Biophysical and Hardware-Efficient Neural Models

verfasst von : Konstantin Selyunin, Ramin M. Hasani, Denise Ratasich, Ezio Bartocci, Radu Grosu

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

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Abstract

In this paper we evaluate how seminal biophysical Hodgkin Huxley model and hardware-efficient TrueNorth model of spiking neurons can be used to perform computations on spike rates in frequency domain. This side-by-side evaluation allows us to draw connections how fundamental arithmetic operations can be realized by means of spiking neurons and what assumptions should be made on input to guarantee the correctness of the computed result. We validated our approach in simulation and consider this work as a first step towards FPGA hardware implementation of neuromorphic accelerators based on spiking models.

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Vorheriges Kapitel Accuracy Improvement of Neural Networks Through Self-Organizing-Maps over Training Datasets

Nächstes Kapitel A SpiNNaker Application: Design, Implementation and Validation of SCPGs

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Titel: Computing with Biophysical and Hardware-Efficient Neural Models
verfasst von: Konstantin Selyunin
Ramin M. Hasani
Denise Ratasich
Ezio Bartocci
Radu Grosu
Verlag: Springer International Publishing
Buch: Advances in Computational Intelligence
Print ISBN: 978-3-319-59152-0

Electronic ISBN: 978-3-319-59153-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59153-7_46

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