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Deterministic modeling of the diffusive memristor

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posted on 22.06.2021, 15:58 by Amir Akther, Yury Ushakov, Alexander Balanov, Sergey Saveliev
Recently developed diffusive memristors have gathered a large amount of research attention due to their unique property to exhibit a variety of spiking regimes reminiscent to that found in biological cells, which creates a great potential for their application in neuromorphic systems of artificial intelligence and unconventional computing. These devices are known to produce a huge range of interesting phenomena through the interplay of regular, chaotic and stochastic behavior. However, the character of these interplays as well as the instabilities responsible for different dynamical regimes are still poorly studied, because of the difficulties in analyzing the complex stochastic dynamics of the memristive devices. In this paper we introduce a new deterministic model justified from the Fokker-Planck description to capture the noise-driven dynamics that noise has been known to produce in the diffusive memristor. This allows us to apply bifurcation theory to reveal the instabilities and the description of the transition between the dynamical regimes.

Funding

Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning

Engineering and Physical Sciences Research Council

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History

School

  • Science

Department

  • Physics

Published in

Chaos

Volume

31

Issue

7

Publisher

AIP Publishing

Version

AM (Accepted Manuscript)

Rights holder

© The authors published under license

Publisher statement

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Akther, A. .. et al., (2021). Deterministic modeling of the diffusive memristor. Chaos 31, 073111 and may be found at https://doi.org/10.1063/5.0056239

Acceptance date

21/06/2021

Publication date

2021-07-07

Copyright date

2021

ISSN

1054-1500

eISSN

1089-7682

Language

en

Depositor

Mr Amir Akther. Deposit date: 21 June 2021

Article number

073111

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Loughborough Publications

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