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Memristor-based neural networks
Open accessAbstract
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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“The modern memristor provides us with just such an adaptive pathway.”
From AHaH Computing–From Metastable Switches to Attractors to Machine Learning (2014) · cites this paper as [83]
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Metadata
- 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
- 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
- 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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