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Spike sorting using non-volatile metal-oxide memristors

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 detection8 (shown previously) and spike sorting at no additional power cost.

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Publication details

The article was accepted on 14 Aug 2018 and first published on 23 Nov 2018


Article type: Paper
DOI: 10.1039/C8FD00130H
Citation: Faraday Discuss., 2018, Accepted Manuscript
  • Open access: Creative Commons BY license
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    Spike sorting using non-volatile metal-oxide memristors

    I. Gupta, A. Serb, A. Khiat, M. Trapatseli and T. Prodromakis, Faraday Discuss., 2018, Accepted Manuscript , DOI: 10.1039/C8FD00130H

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