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Deterministic modeling of the diffusive memristor
journal contribution
posted on 2021-06-22, 15:58 authored by Amir Akther, Yury Ushakov, Alexander BalanovAlexander Balanov, Sergey SavelievSergey SavelievRecently 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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School
- Science
Department
- Physics
Published in
ChaosVolume
31Issue
7Publisher
AIP PublishingVersion
- AM (Accepted Manuscript)
Rights holder
© The authors published under licensePublisher 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/10.1063/5.0056239Acceptance date
2021-06-21Publication date
2021-07-07Copyright date
2021ISSN
1054-1500eISSN
1089-7682Publisher version
Language
- en
Depositor
Amir Akther. Deposit date: 21 June 2021Article number
073111Usage metrics
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