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Experimental demonstration of associative memory with memristive neural networks
Abstract
Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behavior with electronic neural networks.
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“Previously suggested chaotic circuits 19-22 , involving those with memristors 22 , involve several circuit elements.”
From Bistable Nonvolatile Elastic-Membrane Memcapacitor Exhibiting a Chaotic Behavior (2011) · cites this paper as [19]
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Huang, G. M., & Ho, Y. (2014) · Elsevier eBooks · doi:10.1533/9780857098092.2.370 · Cited by 4
Memristive Devices: Switching Effects, Modeling, and Applications
Yang, Y., Chang, T., & Lu, W. D. (2013) · Springer eBooks · doi:10.1007/978-1-4614-9068-5_6 · Cited by 4
Memristor Crossbar Array for Image Storing
Chen, L., Li, C., Huang, T., Wen, S., & Chen, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25393-0_19 · Cited by 4
Associative learning based on symmetric spike time dependent plasticity
Guo, B., Cai, Y., Pan, Y., Zhang, Z., Fang, Y., & Huang, R. (2014) · IEEE International Conference on Solid-State and Integrated Circuit Technology · doi:10.1109/icsict.2014.7021615 · Cited by 4
Metadata
- Authors
- Pershin, Y. V., & Di Ventra, M.
- Affiliations
- University of South Carolina (US)
- Corresponding author
- Pershin, Yuriy V.
- ORCID
- Pershin, Yuriy V.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 23
- Issue
- 7
- Pages
- 881-886
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2015088745
- Cited by
- 1,041
- References
- 44
- Citations by year
- 2026: 26 · 2025: 38 · 2024: 51 · 2023: 53 · 2022: 68 · 2021: 73 · 2020: 84 · 2019: 85 · 2018: 99 · 2017: 61 · 2016: 76 · 2015: 64 · 2014: 93 · 2013: 66 · 2012: 56
- FWCI
- 42.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristors, associative memory, memristive neural networks, artificial synapses, synaptic plasticity, electronic neurons, neuromorphic hardware
- MeSH terms
- Artificial Intelligence, Association Learning, Models, Neurological, Nerve Net, Neuronal Plasticity, Neurons, Synapses, Neural Networks, Computer
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