The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Associative recall of memory without errors
Abstract
A neural network which is capable of recalling without errors any set of linearly independent patterns is studied. The network is based on a Hamiltonian version of the model of Personnaz et al. The energy of a state of N (\ifmmode\pm\else\textpm\fi{}1) neurons is the square of the Euclidean distance---in phase space---between the state and the linear subspace spanned by the patterns. This energy corresponds to nonlocal updatings of the synapses in the learning mode. Results of the mean-field theory (MFT) of the system as well as computer simulations are presented. The stable and metastable states of the network are studied as a function of ``temperature'' T and \ensuremath{\alpha}=p/N, where p is the number of embedded patterns. The maximum capacity of the network is \ensuremath{\alpha}=1. For all \ensuremath{\alpha} (0\ensuremath{\le}\ensuremath{\alpha}<1) the embedded patterns are not only locally stable but are global minima of the energy. The patterns appear, as metastable states, below a temperature T=${T}_{M}$(\ensuremath{\alpha}). The temperature ${T}_{M}$(\ensuremath{\alpha}) decreases to zero as \ensuremath{\alpha}\ensuremath{\rightarrow}1. The spurious states of the network are studied in detail in the case of random uncorrelated patterns. At finite p, they are identical to the mixture states of Hopfield's model. At finite \ensuremath{\alpha}, a spin-glass phase exists as a metastable state. According to the replica symmetric MFT the spin-glass state becomes degenerate with the patterns at \ensuremath{\alpha}=${\ensuremath{\alpha}}_{g}$=1-2/\ensuremath{\pi} and disappears above it. Possible interpretations of this unusual result are discussed. The average radius of attraction R of the patterns has been determined by computer simulations, for sizes up to N=400. The value of R for 0<\ensuremath{\alpha}<1 depends on the details of the dynamics. Results for both parallel and serial dynamics are presented. In both cases R is unity (the largest distance in phase space by definition) at \ensuremath{\alpha}\ensuremath{\rightarrow}0 and decreases monotonically to zero as \ensuremath{\alpha}\ensuremath{\rightarrow}1. Contrary to the MFT, simulations have not revealed, so far, any singularity in the properties of the spurious states at an intermediate value of \ensuremath{\alpha}.
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“The approaches that have been explored lately also include systems for automatic analysis and synthesis of facial expressions [86], [56], [39], [47], [89], [88], [53], [14], [19]”
From Automatic analysis of facial expressions: the state of the art (2000) · cites this paper as [39]
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
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Solvable Model of a Spin-Glass
Sherrington, D., & Kirkpatrick, S. (1975) · Physical Review Letters · doi:10.1103/physrevlett.35.1792 · Cited by 3,577
Solution of 'Solvable model of a spin glass'
Thouless, D. J., Anderson, P. W., & Palmer, R. G. (1977) · Philosophical magazine · doi:10.1080/14786437708235992 · Cited by 1,305
Spin-glass models of neural networks
Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1985) · Physical Review A · doi:10.1103/physreva.32.1007 · Cited by 1,201
Self-citationStoring Infinite Numbers of Patterns in a Spin-Glass Model of Neural Networks
Amit, D. J., Gutfreund, H., & Sompolinsky, H. (1985) · Physical Review Letters · doi:10.1103/physrevlett.55.1530 · Cited by 1,098
Self-citationOrder Parameter for Spin-Glasses
Parisi, G. (1983) · Physical Review Letters · doi:10.1103/physrevlett.50.1946 · Cited by 862
Information storage and retrieval in spin-glass like neural networks
Personnaz, L., Guyon, I., & Dreyfus, G. (1985) · Journal de Physique Lettres · doi:10.1051/jphyslet:01985004608035900 · Cited by 329
Collective properties of neural networks: A statistical physics approach
Peretto, P. (1984) · Biological Cybernetics · doi:10.1007/bf00317939 · Cited by 267
Neural networks with nonlinear synapses and a static noise
Sompolinsky, H. (1986) · Physical Review A · doi:10.1103/physreva.34.2571 · Cited by 230
Self-citationNetworks of Formal Neurons and Memory Palimpsests
Nadal, J., Toulouse, G., Changeux, J., & Dehaene, S. (1986) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/1/10/008 · Cited by 215
Learning and pattern recognition in spin glass models
Kinzel, W. (1985) · The European Physical Journal B · doi:10.1007/bf01304440 · Cited by 149
Physicality of the Little model
Frumkin, A., & Moses, E. (1986) · Physical Review A · doi:10.1103/physreva.34.714 · Cited by 20
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Research works printed but not linked: 2
- 1986, Disordered Systems and Biological Organization
- A. E. Goles, 1986, Disordered Systems and Biological Organization
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Cellular neural networks: theory
Chua, L. O., & Yang, L. (1988) · IEEE Transactions on Circuits and Systems · doi:10.1109/31.7600 · Cited by 4,770
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778
The space of interactions in neural network models
Gardner, E. (1988) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/21/1/030 · Cited by 1,061
“The storage capacity for the Hopfield model for random patterns is p = 0.14N, while the pseudo-inverse (Kohonen 1984, Personnaz et al 1985, Kanter and Sompolinsky 1987) stores N linearly independent patterns.”
MethodOptimal storage properties of neural network models
Gardner, E., & Derrida, B. (1988) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/21/1/031 · Cited by 569
Computational Neuroscience of Vision
Rolls, E. T., & Deco, G. (2001) · doi:10.1093/acprof:oso/9780198524885.001.0001 · Cited by 554
Metadata
- Authors
- Kanter, I., & Sompolinsky, H.
- Affiliations
- Bar-Ilan University (IL)
- Venue
- Physical Review A
- Publisher
- American Physical Society (APS)
- Published
- 1987-01-01
- Volume
- 35
- Issue
- 1
- Pages
- 380-392
- ISSN
- 0556-2791 (ISSN-L)
- OpenAlex
- W2029182425
- Cited by
- 379
- References
- 13
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 13 · 2023: 6 · 2022: 10 · 2021: 9 · 2020: 6 · 2019: 6 · 2018: 1 · 2017: 4 · 2016: 4 · 2015: 2 · 2014: 7 · 2013: 6 · 2012: 6
- FWCI
- 30.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physical Review A (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- associative memory, mean-field theory, metastable states, spin glass phase, spurious states, Hopfield neural network, capacity
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