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A Neuron Model for FPGA Spiking Neuronal Network ImplementationTIGAERU, L.   , BONTEANU, G. |
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Author keywords
spiking neural network, neuromorphics, biological system modeling, field programmable gate arrays, very large scale integration
References keywords
neural(10), networks(9), spiking(7), membrane(5), huxley(5), hodgkin(5), neurons(4), loligo(4), link(4), giant(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 29 - 36
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04005
Web of Science Accession Number: 000297764500005
SCOPUS ID: 84856609800
Abstract
We propose a neuron model, able to reproduce the basic elements of the neuronal dynamics, optimized for digital implementation of Spiking Neural Networks. Its architecture is structured in two major blocks, a datapath and a control unit. The datapath consists of a membrane potential circuit, which emulates the neuronal dynamics at the soma level, and a synaptic circuit used to update the synaptic weight according to the spike timing dependent plasticity (STDP) mechanism. The proposed model is implemented into a Cyclone II-Altera FPGA device. Our results indicate the neuron model can be used to build up 1K Spiking Neural Networks on reconfigurable logic suport, to explore various network topologies. |
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[1] HARDWARE IMPLEMENTATION OF STOCHASTIC SPIKING NEURAL NETWORKS, ROSSELLÓ, JOSEP L., CANALS, VINCENT, MORRO, ANTONI, OLIVER, ANTONI, International Journal of Neural Systems, ISSN 0129-0657, Issue 04, Volume 22, 2012.
Digital Object Identifier: 10.1142/S0129065712500141 [CrossRef]
[2] A spiking neural network for extraction of features in colour opponent visual pathways and FPGA implementation, Sun, Qi Yan, Wu, Qing Xiang, Wang, Xuan, Hou, Lei, Neurocomputing, ISSN 0925-2312, Issue , 2017.
Digital Object Identifier: 10.1016/j.neucom.2016.09.093 [CrossRef]
[3] Implementation of an efficient magnetic tunnel junction-based stochastic neural network with application to iris data classification, Nisar, Arshid, Khanday, Farooq A, Kaushik, Brajesh Kumar, Nanotechnology, ISSN 0957-4484, Issue 50, Volume 31, 2020.
Digital Object Identifier: 10.1088/1361-6528/abadc4 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
