The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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)..
Cited by
Sorted by citations
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing
Wang, Z., Joshi, S., Savel’ev, S. E., Jiang, H., Midya, R., Lin, P., Hu, M., Ge, N., Strachan, J. P., Li, Z., et al. (2017) · Nature Materials · doi:10.1038/nmat4756 · Cited by 2,358
Shafiee, A. R., Nag, A., Muralimanohar, N., Balasubramonian, R., Strachan, J. P., Hu, M., Williams, R. S., & Srikumar, V. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001139 · Cited by 1,554
Synaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Wong, H. ‐. P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,257
Observation of conducting filament growth in nanoscale resistive memories
Yang, Y., Gao, P., Gaba, S., Chang, T., Pan, X., & Lu, W. (2012) · Nature Communications · doi:10.1038/ncomms1737 · Cited by 1,208
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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 ›
Are you an author, or do you see a problem with this page? Request a review
