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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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“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…”
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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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