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Real-time encoding and compression of neuronal spikes by metal-oxide memristors

Real-time encoding and compression of neuronal spikes by metal-oxide memristors
Real-time encoding and compression of neuronal spikes by metal-oxide memristors
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 multi-electrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexander
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Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Zeitler, Ralf
0d1ed289-e01f-4ccf-979b-febc9fa22dc2
Vassanelli, Stefano
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Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Zeitler, Ralf
0d1ed289-e01f-4ccf-979b-febc9fa22dc2
Vassanelli, Stefano
105761d3-6b9b-47ec-a07b-97ad4ef8bd6c
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Gupta, Isha, Serb, Alexander, Khiat, Ali, Zeitler, Ralf, Vassanelli, Stefano and Prodromakis, Themis (2016) Real-time encoding and compression of neuronal spikes by metal-oxide memristors. Nature Communications, 7, [12805]. (doi:10.1038/ncomms12805).

Record type: Article

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 multi-electrode array, demonstrating the technology’s potential for building scalable, yet energy-efficient on-node processors for brain-chip interfaces.

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More information

Accepted/In Press date: 3 August 2016
e-pub ahead of print date: 26 September 2016
Published date: November 2016
Organisations: Faculty of Physical Sciences and Engineering

Identifiers

Local EPrints ID: 399358
URI: http://eprints.soton.ac.uk/id/eprint/399358
PURE UUID: 4609c739-b852-4648-8073-0296ef29b98b
ORCID for Themis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 15 Aug 2016 09:24
Last modified: 27 Jan 2020 13:46

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Contributors

Author: Isha Gupta
Author: Alexander Serb
Author: Ali Khiat
Author: Ralf Zeitler
Author: Stefano Vassanelli

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