Solvable models of working memories
J. Phys. France, 47 9 (1986) 1457-1462
Article cité par
La fonctionnalité Article cité par… liste les citations d'un article. Ces citations proviennent de la base de données des articles de EDP Sciences, ainsi que des bases de données d'autres éditeurs participant au programme CrossRef Cited-by Linking Program. Vous pouvez définir une alerte courriel pour être prévenu de la parution d'un nouvel article citant " cet article (voir sur la page du résumé de l'article le menu à droite).
Article cité :
Citations de cet article :
1 (2025)
https://doi.org/10.1016/B978-0-443-15754-7.00078-X
103 (2025)
https://doi.org/10.1016/B978-0-443-15754-7.00075-4
La vie et l’œuvre de Gérard Toulouse
Bernard Derrida, Marc Mézard, Jean-Pierre Nadal and Yves PomeauComptes Rendus. Physique 26 (G1) 253 (2025)
https://doi.org/10.5802/crphys.240
82 (2025)
https://doi.org/10.1016/B978-0-443-15754-7.00089-4
Glassy dynamics in deep neural networks: A structural comparison
Max Kerr Winter and Liesbeth M. C. JanssenPhysical Review Research 7 (2) (2025)
https://doi.org/10.1103/PhysRevResearch.7.023010
(2024)
https://doi.org/10.1101/2024.01.05.574412
Attractor neural networks with double well synapses
Yu Feng, Nicolas Brunel and Thomas SerrePLOS Computational Biology 20 (2) e1011354 (2024)
https://doi.org/10.1371/journal.pcbi.1011354
A dynamic attractor network model of memory formation, reinforcement and forgetting
Marta Boscaglia, Chiara Gastaldi, Wulfram Gerstner, Rodrigo Quian Quiroga and Marcus KaiserPLOS Computational Biology 19 (12) e1011727 (2023)
https://doi.org/10.1371/journal.pcbi.1011727
Forgetting Leads to Chaos in Attractor Networks
Ulises Pereira-Obilinovic, Johnatan Aljadeff and Nicolas BrunelPhysical Review X 13 (1) (2023)
https://doi.org/10.1103/PhysRevX.13.011009
(2023)
https://doi.org/10.1101/2023.07.17.549266
(2023)
https://doi.org/10.1101/2023.12.01.569560
Bayesian reconstruction of memories stored in neural networks from their connectivity
Sebastian Goldt, Florent Krzakala, Lenka Zdeborová, Nicolas Brunel and Lyle J. GrahamPLOS Computational Biology 19 (1) e1010813 (2023)
https://doi.org/10.1371/journal.pcbi.1010813
Learning and Organization of Memory for Evolving Patterns
Oskar H. Schnaack, Luca Peliti and Armita NourmohammadPhysical Review X 12 (2) (2022)
https://doi.org/10.1103/PhysRevX.12.021063
Supervised learning in the presence of concept drift: a modelling framework
M. Straat, F. Abadi, Z. Kan, et al.Neural Computing and Applications 34 (1) 101 (2022)
https://doi.org/10.1007/s00521-021-06035-1
Stochastic consolidation of lifelong memory
Nimrod Shaham, Jay Chandra, Gabriel Kreiman and Haim SompolinskyScientific Reports 12 (1) (2022)
https://doi.org/10.1038/s41598-022-16407-9
(2021)
https://doi.org/10.1101/2021.10.27.466120
(2021)
https://doi.org/10.1101/2021.08.24.457446
External Stimuli on Neural Networks: Analytical and Numerical Approaches
Evaldo M. F. Curado, Nilo B. Melgar and Fernando D. NobreEntropy 23 (8) 1034 (2021)
https://doi.org/10.3390/e23081034
(2021)
https://doi.org/10.1101/2021.10.13.463791
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization
Michael Biehl, Fthi Abadi, Christina Göpfert and Barbara HammerAdvances in Intelligent Systems and Computing, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization 976 210 (2020)
https://doi.org/10.1007/978-3-030-19642-4_21
Principal component analysis of the magnetic transition in the three-dimensional Fermi-Hubbard model
Ehsan KhatamiJournal of Physics: Conference Series 1290 (1) 012006 (2019)
https://doi.org/10.1088/1742-6596/1290/1/012006
Forgetting Memories and Their Attractiveness
Enzo MarinariNeural Computation 31 (3) 503 (2019)
https://doi.org/10.1162/neco_a_01162
Quality Solution of Logic Programming in Hopfield Neural Network
M S M Kasihmuddin, M A Mansor, S Alzaeemi, M F M Basir and S SathasivamJournal of Physics: Conference Series 1366 (1) 012094 (2019)
https://doi.org/10.1088/1742-6596/1366/1/012094
Statistical Mechanics of On-Line Learning Under Concept Drift
Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer and Michael BiehlEntropy 20 (10) 775 (2018)
https://doi.org/10.3390/e20100775
Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data
Ulises Pereira and Nicolas BrunelNeuron 99 (1) 227 (2018)
https://doi.org/10.1016/j.neuron.2018.05.038
Principal component analysis for fermionic critical points
Natanael C. Costa, Wenjian Hu, Z. J. Bai, Richard T. Scalettar and Rajiv R. P. SinghPhysical Review B 96 (19) (2017)
https://doi.org/10.1103/PhysRevB.96.195138
(2017)
https://doi.org/10.1101/199521
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks
Alireza Alemi, Carlo Baldassi, Nicolas Brunel, Riccardo Zecchina and Peter E. LathamPLOS Computational Biology 11 (8) e1004439 (2015)
https://doi.org/10.1371/journal.pcbi.1004439
Long-Term Memory Stabilized by Noise-Induced Rehearsal
Yi Wei and Alexei A. KoulakovThe Journal of Neuroscience 34 (47) 15804 (2014)
https://doi.org/10.1523/JNEUROSCI.3929-12.2014
Computational modelling of memory retention from synapse to behaviour
Mark C W van Rossum and Maria ShippiJournal of Statistical Mechanics: Theory and Experiment 2013 (03) P03007 (2013)
https://doi.org/10.1088/1742-5468/2013/03/P03007
Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation
Christian Tetzlaff, Christoph Kolodziejski, Marc Timme, et al.PLoS Computational Biology 9 (10) e1003307 (2013)
https://doi.org/10.1371/journal.pcbi.1003307
Embedding Responses in Spontaneous Neural Activity Shaped through Sequential Learning
Tomoki Kurikawa, Kunihiko Kaneko and Tim BehrensPLoS Computational Biology 9 (3) e1002943 (2013)
https://doi.org/10.1371/journal.pcbi.1002943
Time scales of memory, learning, and plasticity
Christian Tetzlaff, Christoph Kolodziejski, Irene Markelic and Florentin WörgötterBiological Cybernetics 106 (11-12) 715 (2012)
https://doi.org/10.1007/s00422-012-0529-z
Properties of Hopfield model with the zero-order synaptic decay
Ryota Miyata, Toru Aonishi, Jun Tsuzurugi and Koji KurataArtificial Life and Robotics 17 (1) 163 (2012)
https://doi.org/10.1007/s10015-012-0033-5
Soft-bound Synaptic Plasticity Increases Storage Capacity
Mark C. W. van Rossum, Maria Shippi, Adam B. Barrett and Peter E. LathamPLoS Computational Biology 8 (12) e1002836 (2012)
https://doi.org/10.1371/journal.pcbi.1002836
Self-consistent signal-to-noise analysis of Hopfield model with unit replacement
Toru Aonishi, Yasunao Komatsu and Koji KurataNeural Networks 23 (10) 1180 (2010)
https://doi.org/10.1016/j.neunet.2010.06.006
Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network
A. Sudo, A. Sato and O. HasegawaIEEE Transactions on Neural Networks 20 (6) 964 (2009)
https://doi.org/10.1109/TNN.2009.2014374
Inhomogeneities in Heteroassociative Memories with Linear Learning Rules
David C. Sterratt and David WillshawNeural Computation 20 (2) 311 (2008)
https://doi.org/10.1162/neco.2007.08-06-301
A property of neural networks of associative memory with replacing units
Akira Date and Koji KurataArtificial Life and Robotics 12 (1-2) 291 (2008)
https://doi.org/10.1007/s10015-007-0484-2
Methods and Models in Neurophysics, École d' été de Physique des Houches Session LXXX
Nicolas BrunelLes Houches, Methods and Models in Neurophysics, École d' été de Physique des Houches Session LXXX 80 407 (2005)
https://doi.org/10.1016/S0924-8099(05)80016-2
Categorization in a Hopfield network trained with weighted examples: Extensive number of concepts
Rogério L. Costa and Alba TheumannPhysical Review E 61 (5) 4860 (2000)
https://doi.org/10.1103/PhysRevE.61.4860
593 (2000)
https://doi.org/10.1109/IJCNN.2000.861381
Categorization in a Hopfield network trained with weighted examples. (I). Finite number of concepts
Rogério L. Costa and Alba TheumannPhysica A: Statistical Mechanics and its Applications 268 (3-4) 499 (1999)
https://doi.org/10.1016/S0378-4371(99)00043-6
Recall of old and recent information
Vipin Srivastava, Meena Vipin and Enzo GranatoNetwork: Computation in Neural Systems 9 (2) 159 (1998)
https://doi.org/10.1088/0954-898X_9_2_001
Recall of old and recent information
Vipin Srivastava, Meena Vipin and Enzo GranatoNetwork: Computation in Neural Systems 9 (2) 159 (1998)
https://doi.org/10.1088/0954-898X/9/2/001
Paramagnetic unlearning in neural network models
Kazuo NokuraPhysical Review E 54 (5) 5571 (1996)
https://doi.org/10.1103/PhysRevE.54.5571
Models of Neural Networks I
J. Leo van Hemmen and Reimer KühnPhysics of Neural Networks, Models of Neural Networks I 1 (1995)
https://doi.org/10.1007/978-3-642-79814-6_1
A memory model with novel behaviour in sequential learning
P Kahn, K Wong and D SherringtonNetwork: Computation in Neural Systems 6 (3) 415 (1995)
https://doi.org/10.1088/0954-898X/6/3/007
A memory model with novel behaviour in sequential learning
P E Kahn, K Y M Wong and D SherringtonNetwork: Computation in Neural Systems 6 (3) 415 (1995)
https://doi.org/10.1088/0954-898X_6_3_007
Domain Theory in Learning Processes
A. EdalatElectronic Notes in Theoretical Computer Science 1 114 (1995)
https://doi.org/10.1016/S1571-0661(04)80007-0
Effects of temporary synaptic strengthening and residual cell potential in the retrieval of patterns
Tohru Nakano and Osamu MoriyamaNetwork: Computation in Neural Systems 5 (2) 229 (1994)
https://doi.org/10.1088/0954-898X_5_2_007
4 2454 (1994)
https://doi.org/10.1109/ICNN.1994.374605
Stochastic neural networks with the weighted Hebb rule
Caren Marzban and Raju ViswanathanPhysics Letters A 191 (1-2) 127 (1994)
https://doi.org/10.1016/0375-9601(94)90570-3
Effects of temporary synaptic strengthening and residual cell potential in the retrieval of patterns
Tohru Nakano and Osamu MoriyamaNetwork: Computation in Neural Systems 5 (2) 229 (1994)
https://doi.org/10.1088/0954-898X/5/2/007
ICANN ’93
Shotaro AkahoICANN ’93 707 (1993)
https://doi.org/10.1007/978-1-4471-2063-6_198
Learning drifting concepts with neural networks
M Biehl and H SchwarzeJournal of Physics A: Mathematical and General 26 (11) 2651 (1993)
https://doi.org/10.1088/0305-4470/26/11/014
The statistical mechanics of learning a rule
Timothy L. H. Watkin, Albrecht Rau and Michael BiehlReviews of Modern Physics 65 (2) 499 (1993)
https://doi.org/10.1103/RevModPhys.65.499
Multifractality in forgetful memories
U. Behn, J.L. van Hemmen, R. Kühn, A. Lange and V.A. ZagrebnovPhysica D: Nonlinear Phenomena 68 (3-4) 401 (1993)
https://doi.org/10.1016/0167-2789(93)90133-L
Neural network with hierarchical clustering near saturation
M A Pires Idiart and A TheumannJournal of Physics A: Mathematical and General 25 (4) 779 (1992)
https://doi.org/10.1088/0305-4470/25/4/017
Constraints on learning in dynamic synapses
Daniel J Amit and Stefano FusiNetwork: Computation in Neural Systems 3 (4) 443 (1992)
https://doi.org/10.1088/0954-898X_3_4_008
Constraints on learning in dynamic synapses
Daniel Amit and Stefano FusiNetwork: Computation in Neural Systems 3 (4) 443 (1992)
https://doi.org/10.1088/0954-898X/3/4/008
On-Line Learning of a Time-Dependent Rule
M Biehl and H SchwarzeEurophysics Letters (EPL) 20 (8) 733 (1992)
https://doi.org/10.1209/0295-5075/20/8/012
Neural network models of list learning
Neil Burgess, J L Shapiro and M A MooreNetwork: Computation in Neural Systems 2 (4) 399 (1991)
https://doi.org/10.1088/0954-898X_2_4_005
A neural network model of working memory exhibiting primacy and recency
K Y M Wong, P E Kahn and D SherringtonJournal of Physics A: Mathematical and General 24 (5) 1119 (1991)
https://doi.org/10.1088/0305-4470/24/5/025
Unsupervised dynamic learning in layered neural networks
H J J Jonker and A C C CoolenJournal of Physics A: Mathematical and General 24 (17) 4219 (1991)
https://doi.org/10.1088/0305-4470/24/17/032
Neural network models of list learning
Neil Burgess, J Shapiro and M MooreNetwork: Computation in Neural Systems 2 (4) 399 (1991)
https://doi.org/10.1088/0954-898X/2/4/005
Models of Neural Networks
J. Leo van Hemmen and Reimer KühnPhysics of Neural Networks, Models of Neural Networks 1 (1991)
https://doi.org/10.1007/978-3-642-97171-6_1
Statistical Mechanics of Neural Networks
Stam NicolisLecture Notes in Physics, Statistical Mechanics of Neural Networks 368 155 (1990)
https://doi.org/10.1007/3540532676_47
Learning in neural network memories
L F AbbottNetwork: Computation in Neural Systems 1 (1) 105 (1990)
https://doi.org/10.1088/0954-898X_1_1_008
Statistical Mechanics of Neural Networks
B. Schürmann, J. Hollatz and U. RamacherLecture Notes in Physics, Statistical Mechanics of Neural Networks 368 49 (1990)
https://doi.org/10.1007/3540532676_40
Retrieval Properties of Neural Networks with Infinitely Many Marked Patterns
S NicolisEurophysics Letters (EPL) 12 (7) 583 (1990)
https://doi.org/10.1209/0295-5075/12/7/002
Selective pattern recall in neural networks by chemical modulation
A C C Coolen and A J NoestJournal of Physics A: Mathematical and General 23 (4) 575 (1990)
https://doi.org/10.1088/0305-4470/23/4/026
2 971 (1990)
https://doi.org/10.1109/ACSSC.1990.523482
Learning in neural network memories
L AbbottNetwork: Computation in Neural Systems 1 (1) 105 (1990)
https://doi.org/10.1088/0954-898X/1/1/008
Neural networks as content addressable memories and learning machines
Roland KöberleComputer Physics Communications 56 (1) 43 (1989)
https://doi.org/10.1016/0010-4655(89)90051-9
Information processing in three-state neural networks
C. Meunier, D. Hansel and A. VergaJournal of Statistical Physics 55 (5-6) 859 (1989)
https://doi.org/10.1007/BF01041070
Relative stability in the dynamics of a two-pattern neural net
F Pazmandi and T GesztiJournal of Physics A: Mathematical and General 22 (23) 5117 (1989)
https://doi.org/10.1088/0305-4470/22/23/016
Dynamic Interactions in Neural Networks: Models and Data
Shigeru ShinomotoResearch Notes in Neural Computing, Dynamic Interactions in Neural Networks: Models and Data 1 73 (1989)
https://doi.org/10.1007/978-1-4612-4536-0_5
Neuronal models of cognitive functions
Jean-Pierre Changeux and Stanislas DehaeneCognition 33 (1-2) 63 (1989)
https://doi.org/10.1016/0010-0277(89)90006-1
Neural networks as models of associative memories
N. PargaComputer Physics Communications 55 (1) 77 (1989)
https://doi.org/10.1016/0010-4655(89)90065-9
Spin glasses and neural networks
Nestor PargaNuclear Physics B - Proceedings Supplements 10 (1) 332 (1989)
https://doi.org/10.1016/0920-5632(89)90071-6
Iterated learning in a layered feed-forward neural network
Ronny Meir and Eytan DomanyPhysical Review A 37 (7) 2660 (1988)
https://doi.org/10.1103/PhysRevA.37.2660
Enhancing the learning of a finite number of patterns in neural networks
J F Fontanari and R KoberleJournal of Physics A: Mathematical and General 21 (4) L253 (1988)
https://doi.org/10.1088/0305-4470/21/4/012
A Short-Term Neural Network Memory
Robert J. T. Morris and Wing Shing WongSIAM Journal on Computing 17 (6) 1103 (1988)
https://doi.org/10.1137/0217071
Glauber dynamics of the Little-Hopfield model
H. Rieger, M. Schreckenberg and J. ZittartzZeitschrift f�r Physik B Condensed Matter 72 (4) 523 (1988)
https://doi.org/10.1007/BF01314534
Introduction to neural network models
B. DerridaNuclear Physics B - Proceedings Supplements 4 673 (1988)
https://doi.org/10.1016/0920-5632(88)90175-2
Nonlinear neural networks. II. Information processing
J. L. van Hemmen, D. Grensing, A. Huber and R. KühnJournal of Statistical Physics 50 (1-2) 259 (1988)
https://doi.org/10.1007/BF01022995
Content-addressability and learning in neural networks
B M ForrestJournal of Physics A: Mathematical and General 21 (1) 245 (1988)
https://doi.org/10.1088/0305-4470/21/1/029
199 (1988)
https://doi.org/10.1109/ICNN.1988.23848
Potts-glass models of neural networks
Ido KanterPhysical Review A 37 (7) 2739 (1988)
https://doi.org/10.1103/PhysRevA.37.2739
Layered feed-forward neural network with exactly soluble dynamics
Ronny Meir and Eytan DomanyPhysical Review A 37 (2) 608 (1988)
https://doi.org/10.1103/PhysRevA.37.608
Spin glass model for a neural network : associative memories stored with unequal weights
L. VianaJournal de Physique 49 (2) 167 (1988)
https://doi.org/10.1051/jphys:01988004902016700
Extensions of a solvable feed forward neural network
Ronny MeirJournal de Physique 49 (2) 201 (1988)
https://doi.org/10.1051/jphys:01988004902020100
Optimal storage properties of neural network models
E Gardner and B DerridaJournal of Physics A: Mathematical and General 21 (1) 271 (1988)
https://doi.org/10.1088/0305-4470/21/1/031
The space of interactions in neural network models
E GardnerJournal of Physics A: Mathematical and General 21 (1) 257 (1988)
https://doi.org/10.1088/0305-4470/21/1/030
Maximum Storage Capacity in Neural Networks
E GardnerEurophysics Letters (EPL) 4 (4) 481 (1987)
https://doi.org/10.1209/0295-5075/4/4/016
Information storage in neural networks with low levels of activity
Daniel J. Amit, Hanoch Gutfreund and H. SompolinskyPhysical Review A 35 (5) 2293 (1987)
https://doi.org/10.1103/PhysRevA.35.2293
Time-Dependent Effects in Disordered Materials
Gérard ToulouseTime-Dependent Effects in Disordered Materials 359 (1987)
https://doi.org/10.1007/978-1-4684-7476-3_38
Memory maintenance in neural networks
S ShinomotoJournal of Physics A: Mathematical and General 20 (18) L1305 (1987)
https://doi.org/10.1088/0305-4470/20/18/015
Heidelberg Colloquium on Glassy Dynamics
Daniel J. AmitLecture Notes in Physics, Heidelberg Colloquium on Glassy Dynamics 275 429 (1987)
https://doi.org/10.1007/BFb0057530