Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses w...

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Main Authors:	Werner, Thilo, Vianello, Elisa, Bichler, Olivier, Garbin, Daniele, Cattaert, Daniel, Yvert, Blaise, De Salvo, Barbara, Perniola, Luca
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093145/

id	pubmed-5093145
recordtype	oai_dc
spelling	pubmed-50931452016-11-17 Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting Werner, Thilo Vianello, Elisa Bichler, Olivier Garbin, Daniele Cattaert, Daniel Yvert, Blaise De Salvo, Barbara Perniola, Luca Neuroscience In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. Frontiers Media S.A. 2016-11-03 /pmc/articles/PMC5093145/ /pubmed/27857680 http://dx.doi.org/10.3389/fnins.2016.00474 Text en Copyright © 2016 Werner, Vianello, Bichler, Garbin, Cattaert, Yvert, De Salvo and Perniola. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Werner, Thilo Vianello, Elisa Bichler, Olivier Garbin, Daniele Cattaert, Daniel Yvert, Blaise De Salvo, Barbara Perniola, Luca
spellingShingle	Werner, Thilo Vianello, Elisa Bichler, Olivier Garbin, Daniele Cattaert, Daniel Yvert, Blaise De Salvo, Barbara Perniola, Luca Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
author_facet	Werner, Thilo Vianello, Elisa Bichler, Olivier Garbin, Daniele Cattaert, Daniel Yvert, Blaise De Salvo, Barbara Perniola, Luca
author_sort	Werner, Thilo
title	Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
title_short	Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
title_full	Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
title_fullStr	Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
title_full_unstemmed	Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting
title_sort	spiking neural networks based on oxram synapses for real-time unsupervised spike sorting
description	In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.
publisher	Frontiers Media S.A.
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093145/
_version_	1613710476912361472

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

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