The University of Southampton
University of Southampton Institutional Repository

Dataset for Real-time encoding and compression of neuronal spikes by metal-oxide memristor

Dataset for Real-time encoding and compression of neuronal spikes by metal-oxide memristor
Dataset for Real-time encoding and compression of neuronal spikes by metal-oxide memristor
Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here, we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multielectrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.
University of Southampton
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Vassanelli, Stefano
105761d3-6b9b-47ec-a07b-97ad4ef8bd6c
Zeitler, Ralf
0d1ed289-e01f-4ccf-979b-febc9fa22dc2
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Vassanelli, Stefano
105761d3-6b9b-47ec-a07b-97ad4ef8bd6c
Zeitler, Ralf
0d1ed289-e01f-4ccf-979b-febc9fa22dc2

Gupta, Isha, Serb, Alexantrou, Khiat, Ali, Prodromakis, Themistoklis, Vassanelli, Stefano and Zeitler, Ralf (2016) Dataset for Real-time encoding and compression of neuronal spikes by metal-oxide memristor. University of Southampton doi:10.5258/SOTON/400411 [Dataset]

Record type: Dataset

Abstract

Advanced brain-chip interfaces with numerous recording sites bear great potential for investigation of neuroprosthetic applications. The bottleneck towards achieving an efficient bio-electronic link is the real-time processing of neuronal signals, which imposes excessive requirements on bandwidth, energy and computation capacity. Here, we present a unique concept where the intrinsic properties of memristive devices are exploited to compress information on neural spikes in real time. We demonstrate that the inherent voltage thresholds of metal-oxide memristors can be used for discriminating recorded spiking events from background activity and without resorting to computationally heavy off-line processing. We prove that information on spike amplitude and frequency can be transduced and stored in single devices as non-volatile resistive state transitions. Finally, we show that a memristive device array allows for efficient data compression of signals recorded by a multielectrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

Archive
Dataset.zip - Dataset
Download (260MB)

More information

Published date: 2016
Organisations: Electronics & Computer Science, Nanoelectronics and Nanotechnology
Projects:
Reliably unreliable nanotechnologies
Funded by: UNSPECIFIED (EP/K017829/1)
2 September 2013 to 1 September 2018
Real neurons-nanoelectronics Architecture with Memristive Plasticity (RAMP)
Funded by: UNSPECIFIED (612058)
1 November 2013 to 31 October 2016

Identifiers

Local EPrints ID: 400411
URI: https://eprints.soton.ac.uk/id/eprint/400411
PURE UUID: 1a4b66df-d123-40c7-9ca2-96d164cbf62e
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 14 Sep 2016 12:50
Last modified: 06 Jun 2018 12:24

Export record

Altmetrics

Contributors

Creator: Isha Gupta
Creator: Alexantrou Serb
Creator: Ali Khiat
Creator: Stefano Vassanelli
Creator: Ralf Zeitler

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×