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Spike sorting using non-volatile metal-oxide memristors

Spike sorting using non-volatile metal-oxide memristors
Spike sorting using non-volatile metal-oxide memristors

Electrophysiological techniques have improved substantially over the past years to the point that neuroprosthetics applications are becoming viable. This evolution has been fuelled by the advancement of implantable microelectrode technologies that have followed their own version of Moore's scaling law. Similarly to electronics, however, excessive data-rates and strained power budgets require the development of more efficient computation paradigms for handling neural data in situ; in particular the computationally heavy task of events classification. Here, we demonstrate how the intrinsic analogue programmability of memristive devices can be exploited to perform spike-sorting on single devices. Leveraging the physical properties of nanoscale memristors allows us to demonstrate that these devices can capture enough information in neural signal for performing spike detection (shown previously) and spike sorting at no additional power cost.

1359-6640
511-520
Gupta, Isha
11f9ea1a-e38a-45d4-930d-96ac78b3d734
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Khiat, Ali
bf549ddd-5356-4a7d-9c12-eb6c0d904050
Trapatseli, Maria
1aea9f6b-2790-48b4-85d5-e600e60f6406
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
Trapatseli, Maria
1aea9f6b-2790-48b4-85d5-e600e60f6406
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Gupta, Isha, Serb, Alexantrou, Khiat, Ali, Trapatseli, Maria and Prodromakis, Themistoklis (2019) Spike sorting using non-volatile metal-oxide memristors. Faraday Discussions, 213, 511-520. (doi:10.1039/c8fd00130h).

Record type: Article

Abstract

Electrophysiological techniques have improved substantially over the past years to the point that neuroprosthetics applications are becoming viable. This evolution has been fuelled by the advancement of implantable microelectrode technologies that have followed their own version of Moore's scaling law. Similarly to electronics, however, excessive data-rates and strained power budgets require the development of more efficient computation paradigms for handling neural data in situ; in particular the computationally heavy task of events classification. Here, we demonstrate how the intrinsic analogue programmability of memristive devices can be exploited to perform spike-sorting on single devices. Leveraging the physical properties of nanoscale memristors allows us to demonstrate that these devices can capture enough information in neural signal for performing spike detection (shown previously) and spike sorting at no additional power cost.

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More information

Accepted/In Press date: 14 August 2018
e-pub ahead of print date: 23 November 2018
Published date: 1 February 2019

Identifiers

Local EPrints ID: 428616
URI: https://eprints.soton.ac.uk/id/eprint/428616
ISSN: 1359-6640
PURE UUID: a0d3a4c8-59e4-4f30-9c6a-2be1e19b5497
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 05 Mar 2019 17:30
Last modified: 14 Mar 2019 01:34

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Contributors

Author: Isha Gupta
Author: Alexantrou Serb
Author: Ali Khiat
Author: Maria Trapatseli

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