The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Storage capacity of attractor neural networks with depressing synapses
Open accessAbstract
We compute the capacity of a binary neural network with dynamic depressing synapses to store and retrieve an infinite number of patterns. We use a biologically motivated model of synaptic depression and a standard mean-field approach. We find that at T=0 the critical storage capacity decreases with the degree of the depression. We confirm the validity of our main mean-field results with numerical simulations.
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“Using tricks-of-trade and allowing small retrieval errors, the storage capacity is about 0 . 138 d (Crisanti et al., 1986; Hertz et al., 1991; Torres et al., 2002).”
From Hopfield Networks is All You Need (2020) · cites this paper as Torres et al., 2002
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Cites
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Synaptic depression and cortical gain control
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Associative Memory with Dynamic Synapses
Pantic, L., Torres, J. J., Kappen, H. J., & Gielen, S. C. A. M. (2002) · Neural Computation · doi:10.1162/089976602760805331 · Cited by 96
Self-citationEfficient temporal processing with biologically realistic dynamic synapses
Natschlger, T., Maass, W., & Zador, A. M. (2001) · Network Computation in Neural Systems · doi:10.1088/0954-898x/12/1/305 · Cited by 67
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Research works printed but not linked: 1
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Cited by
Hopfield Networks is All You Need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv · doi:10.48550/arxiv.2008.02217 · Cited by 236
“ern or C<1=4 if all patterns are stable [45]. The same bound holds for nonlinear learning rules [44]. Using tricks-of-trade and allowing small retrieval errors, the storage capacity is about 0:138d[19, 33, 63]. If the learning rule is not related to the Hebb rule then up to dpatterns can be stored [1]. Using a Hopfi…”
BackgroundHuber, D. E., & O’Reilly, R. C. (2003) · Cognitive Science · doi:10.1016/s0364-0213(03)00012-0 · Cited by 110
Associative Memory with Dynamic Synapses
Pantic, L., Torres, J. J., Kappen, H. J., & Gielen, S. C. A. M. (2002) · Neural Computation · doi:10.1162/089976602760805331 · Cited by 96
Self-citationA working memory model based on fast Hebbian learning
Sandberg, A., Tegner, J. N., & Lansner, A. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/4/309 · Cited by 93
Maximum Memory Capacity on Neural Networks with Short-Term Synaptic Depression and Facilitation
Mejías, J. F., & Torres, J. J. (2008) · Neural Computation · doi:10.1162/neco.2008.02-08-719 · Cited by 53
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Metadata
- Authors
- Torres, J. J., Pantic, L., & Kappen, H. J.
- Affiliations
- Radboud University Nijmegen (NL)
- ORCID
- Kappen, Hilbert J.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2002-12-20
- Volume
- 66
- Issue
- 6
- Pages
- 061910-061910
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W2110163023
- Cited by
- 33
- References
- 14
- Citations by year
- 2022: 2 · 2021: 1 · 2020: 1 · 2019: 1 · 2017: 3 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Keywords
- storage capacity, attractor networks, synaptic depression, binary neural networks, mean-field theory, zero temperature
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- Torres, J. J., Pantic, L., & Kappen, H. J. (2002). Storage capacity of attractor neural networks with depressing synapses. Physical Review E. https://doi.org/10.1103/physreve.66.061910
