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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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Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008) · Nature · doi:10.1038/nature06932 · Cited by 11,828
Memristor—The Missing Circuit Element
Chua, L. (1971) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1971.1083337 · Cited by 10,268 · cited 1×
“for short [20], [21]) based on TiO 2 thin films [22], [23] offers a promising realization of a synapse whose size can be as small as 30×30×2 nm(3).”
BackgroundMemristive switching mechanism for metal/oxide/metal nanodevices
Yang, J. J., Pickett, M. D., Li, X., Ohlberg, D. A. A., Stewart, D. R., & Williams, R. S. (2008) · Nature Nanotechnology · doi:10.1038/nnano.2008.160 · Cited by 2,987
Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex.
Pavlov, P. I. (1930) · Archives of Neurology And Psychiatry · doi:10.1001/archneurpsyc.1930.02220110256018 · Cited by 2,681
Memristive devices and systems
Chua, L. O., & Kang, S. M. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10092 · Cited by 2,604 · cited 3×
“,t is a generalized response (memresistance R , or memductance G ), x is a ndimensional vector describing the internal state of the device, and f ()x,u,t is a continuous n-dimensional vector function [7, 8]. As Fig. 2a illustrates schematically, our memristor emulator consists of the following units: a digital potenti…”
BackgroundInfluentialAnderson, J. R. (2013) · Psychology Press eBooks · doi:10.4324/9780203780954 · Cited by 2,180
Neural Networks: A Systematic Introduction
Rojas, R. (1996) · Medical Entomology and Zoology · Cited by 2,142
Conditioned Reflexes: an Investigation of the Physiological Activity of the Cerebral Cortex
Denny-Brown, D. E. (1928) · Nature · doi:10.1038/121662a0 · Cited by 1,854
Research works printed but not linked: 4
- Arthur, 2006, Vol. 18, 75
- Bofill, 2001, Vol. 14, 1091
- Snider, 2008, Cortical computing with memristive nanodevices, SciDAC Review, 10, 58
- Vogelstein, R., Malik, U., & Cauwenberghs, G. (2007). Silicon spikebased synaptic array and address-event transceiver. In Proceedings of ISCAS04: Vol. 5 (pp. 385–388)..
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Dong, Z., Dong-lian, Q., Luo, L., ShuKai, D., & Hu, X. (2016) · UKACC International Conference on Control · doi:10.1109/control.2016.7737525 · Cited by 1
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Tan, A. J. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1
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Sinha, A. (2000) · doi:10.15760/etd.445 · Cited by 1
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Fitch, A. L. (2014) · UWA Profiles and Research Repository (UWA)
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Kohlstedt, H., Ziegler, M., Soni, R., Patelczyk, T., Ignatov, M., Bartsch, T., Meuffels, P., Chang, T., Jo, S., Sheridan, P. M., et al. (2012) · Frontiers in Electronic Materials · doi:10.1002/9783527667703.ch41
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Research on emergent physical properties of integrated nanomaterials
リ, チ., & Li, Q. (2020)
Wu, F., & Huang, Y. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9549629
Exponentially Mean Stability Analysis of Positive Markov Jump Neural Networks with Time DelayPreprint
Yin, K., Yang, D., Liu, J., Li, H., & Wang, L. (2021) · Research Square · doi:10.21203/rs.3.rs-942126/v1
Holy memristor.Preprint
Kim, J., Dowling, V. J., Datta, T., & Pershin, Y. V. (2021) · arXiv (Cornell University)
Self-citationZhu, J., Liu, H., Tao, H., & Tan, H. (2021) · International Conference on Neuromorphic Computing (ICNC) · doi:10.1109/icnc52316.2021.9608583
Yu, H., & Wang, Y. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0551-5_4
Pattern Recognition with Antiferromagnet-heavy Metal Hybrid Structure
Mitrofanova, A., Safin, A. R., Egorov, D. P., Kravchenko, O. V., & Bazenkov, N. I. (2021) · Photonics & Electromagnetics Research Symposium (PIERS) · doi:10.1109/piers53385.2021.9694780
Ansari, M. S. (2013) · Studies in computational intelligence · doi:10.1007/978-81-322-1563-9_1
Lee, T. (2018) · Springer theses · doi:10.1007/978-981-13-2535-9_2
Thomas, A., & Kaltschmidt, C. (2019) · Springer eBooks · doi:10.1007/978-3-319-76375-0_21
Katz, E. (2019) · doi:10.1002/9783527819997.ch8
Njimah, O. M., Janarthanan, R., Kengne, J., & Telem, A. N. K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4153585
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Singh, A. (2022) · doi:10.36227/techrxiv.20318268
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Singh, A. (2022) · doi:10.36227/techrxiv.20318268.v1
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Kohl, Z., Vávra, J., & Bužga, J. (2022) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme55909.2022.9988188
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Ventra, M. D., & Pershin, Y. V. (2023) · SpringerBriefs in physics · doi:10.1007/978-3-031-25625-7_5
Self-citationLiu, L., & Bao, H. (2022) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc55256.2022.10033919
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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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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