Solvable models of working memories
J. Phys. France, 47 9 (1986) 1457-1462
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(2017)
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593 (2000)
https://doi.org/10.1109/IJCNN.2000.861381
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4 2454 (1994)
https://doi.org/10.1109/ICNN.1994.374605
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https://doi.org/10.1007/978-1-4471-2063-6_198
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https://doi.org/10.1103/RevModPhys.65.499
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https://doi.org/10.1088/0954-898X_3_4_008
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https://doi.org/10.1088/0305-4470/25/4/017
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https://doi.org/10.1088/0954-898X/3/4/008
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https://doi.org/10.1209/0295-5075/20/8/012
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
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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
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https://doi.org/10.1007/978-3-642-97171-6_1
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https://doi.org/10.1088/0305-4470/24/5/025
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https://doi.org/10.1088/0954-898X_2_4_005
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https://doi.org/10.1007/3540532676_40
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
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
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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
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
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
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
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Roland KöberleComputer Physics Communications 56 (1) 43 (1989)
https://doi.org/10.1016/0010-4655(89)90051-9
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
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
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
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
199 (1988)
https://doi.org/10.1109/ICNN.1988.23848
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
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
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Ronny Meir and Eytan DomanyPhysical Review A 37 (7) 2660 (1988)
https://doi.org/10.1103/PhysRevA.37.2660
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
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
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
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
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
Potts-glass models of neural networks
Ido KanterPhysical Review A 37 (7) 2739 (1988)
https://doi.org/10.1103/PhysRevA.37.2739
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
Extensions of a solvable feed forward neural network
Ronny MeirJournal de Physique 49 (2) 201 (1988)
https://doi.org/10.1051/jphys:01988004902020100
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
Introduction to neural network models
B. DerridaNuclear Physics B - Proceedings Supplements 4 673 (1988)
https://doi.org/10.1016/0920-5632(88)90175-2
Stochastic Dynamics of a Layered Neural Network; Exact Solution
R Meir and E DomanyEurophysics Letters (EPL) 4 (6) 645 (1987)
https://doi.org/10.1209/0295-5075/4/6/002
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
Response : Computing with Neural Networks
John J. Hopfield and David W. TankScience 235 (4793) 1228 (1987)
https://doi.org/10.1126/science.235.4793.1228
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