The University of Southampton
University of Southampton Institutional Repository

Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes

Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes
Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

Integrating sensor, memristors, metastable resistive state, neural recordings, RRAM, volatility, volatility module
1932-4545
351-359
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
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, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
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, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Gupta, Isha, Serb, Alexantrou, Khiat, Ali, Zeitler, Ralf, Vassanelli, Stefano and Prodromakis, Themistoklis (2018) Sub 100 nW volatile nano-metal-oxide memristor as synaptic-like encoder of neuronal spikes. IEEE Transactions on Biomedical Circuits and Systems, 12 (2), 351-359. (doi:10.1109/TBCAS.2018.2797939).

Record type: Article

Abstract

Advanced neural interfaces mediate a bioelectronic link between the nervous system and microelectronic devices, bearing great potential as innovative therapy for various diseases. Spikes from a large number of neurons are recorded leading to creation of big data that require online processing under most stringent conditions, such as minimal power dissipation and on-chip space occupancy. Here, we present a new concept where the inherent volatile properties of a nano-scale memristive device are used to detect and compress information on neural spikes as recorded by a multielectrode array. Simultaneously, and similarly to a biological synapse, information on spike amplitude and frequency is transduced in metastable resistive state transitions of the device, which is inherently capable of self-resetting and of continuous encoding of spiking activity. Furthermore, operating the memristor in a very high resistive state range reduces its average in-operando power dissipation to less than 100 nW, demonstrating the potential to build highly scalable, yet energy-efficient on-node processors for advanced neural interfaces.

Text
08305535 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 21 January 2018
e-pub ahead of print date: 1 March 2018
Published date: April 2018
Keywords: Integrating sensor, memristors, metastable resistive state, neural recordings, RRAM, volatility, volatility module

Identifiers

Local EPrints ID: 421127
URI: https://eprints.soton.ac.uk/id/eprint/421127
ISSN: 1932-4545
PURE UUID: 85694ca7-4fe0-4c7c-b059-ccb708638de1
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 22 May 2018 16:30
Last modified: 14 Mar 2019 01:34

Export record

Altmetrics

Contributors

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

University divisions

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.

×