eprintid: 10042981
rev_number: 24
eprint_status: archive
userid: 608
dir: disk0/10/04/29/81
datestamp: 2018-02-12 15:02:16
lastmod: 2020-05-09 03:08:54
status_changed: 2018-02-12 15:02:16
type: article
metadata_visibility: show
creators_name: Zarudnyi, K
creators_name: Mehonic, A
creators_name: Montesi, L
creators_name: Buckwell, M
creators_name: Hudziak, S
creators_name: Kenyon, AJ
title: Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices
ispublished: pub
divisions: UCL
divisions: A01
divisions: B04
divisions: C05
divisions: F43
divisions: F46
keywords: Resistive switching, resistance switching, STDP, RRAM, machine learning, neuromorphic systems
note: © 2018 Zarudnyi, Mehonic, Montesi, Buckwell, Hudziak and Kenyon.
This is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner are credited
and that the original publication in this journal is cited, in accordance with accepted
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abstract: Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiOx) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.
date: 2018-02-08
date_type: published
publisher: Frontiers Media
official_url: http://dx.doi.org/10.3389/fnins.2018.00057
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1533272
doi: 10.3389/fnins.2018.00057
lyricists_name: Buckwell, Mark
lyricists_name: Hudziak, Steve
lyricists_name: Kenyon, Anthony
lyricists_name: Mehonic, Adnan
lyricists_name: Montesi, Luca
lyricists_id: MBUCK33
lyricists_id: SHUDZ49
lyricists_id: AJKEN86
lyricists_id: AMEHO63
lyricists_id: LMONT04
actors_name: Kenyon, Anthony
actors_id: AJKEN86
actors_role: owner
full_text_status: public
publication: Frontiers in Neuroscience
volume: 12
article_number: 57
issn: 1662-4548
citation:        Zarudnyi, K;    Mehonic, A;    Montesi, L;    Buckwell, M;    Hudziak, S;    Kenyon, AJ;      (2018)    Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices.                   Frontiers in Neuroscience , 12     , Article 57.  10.3389/fnins.2018.00057 <https://doi.org/10.3389/fnins.2018.00057>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10042981/1/Frontiers%20Neuroscience%202018.pdf