Cited by 471Advanced Memory and Neural ComputingNeural Networks and ApplicationsNeural dynamics and brain function

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Memristor-based neural networks

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Thomas, Andy·2013·Journal of Physics D: Applied Physics

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Abstract

The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them.

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Authors: Thomas, A.
Affiliations: Universität Hamburg (DE)
Corresponding author: Thomas, Andy
ORCID: Thomas, Andy
Venue: Journal of Physics D: Applied Physics
Publisher: IOP Publishing
Published: 2013-02-05
Volume: 46
Issue: 9
Pages: 093001
ISSN: 0022-3727 (ISSN-L)
1361-6463
DOI: 10.1088/0022-3727/46/9/093001
OpenAlex: W2159543793
Cited by: 471
References: 141
Citations by year: 2026: 10 · 2025: 41 · 2024: 50 · 2023: 51 · 2022: 40 · 2021: 43 · 2020: 32 · 2019: 37 · 2018: 42 · 2017: 31 · 2016: 31 · 2015: 26 · 2014: 22 · 2013: 13
FWCI: 18.40
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: iopscience.iop.org/info/page/text-and-data-mining (tdm)
iopscience.iop.org/page/copyright (vor)
Locations: Journal of Physics D Applied Physics (publishedVersion)
PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords: memristors, artificial neural networks, synaptic plasticity, long-term potentiation, spike-timing-dependent plasticity, neuromorphic hardware, electronic synapses, biological neural networks, electric circuit model

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