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Network: Computation in Neural Systems Volume 3, 1992 - Issue 4
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Original Article

Constraints on learning in dynamic synapses

Daniel J Amit INFN, Sezione di Roma, Istituto di Fisica, Università di Roma, 'La Sapienza', P le Aldo Moro, I-00185, Roma, Italy
&
Stefano Fusi INFN, Sezione di Roma, Istituto di Fisica, Università di Roma, 'La Sapienza', P le Aldo Moro, I-00185, Roma, Italy
Pages 443-464 | Received 01 Jun 1992, Published online: 09 Jul 2009

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Anita Mehta. (2018) Storing and retrieving long-term memories: cooperation and competition in synaptic dynamics. Advances in Physics: X 3:1.
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Nicolas Brunel. (2008) Daniel Amit (1938–2007). Network: Computation in Neural Systems 19:1, pages 3-8.
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M. Coath, J. M. Brader, S. Fusi & S. L. Denham. (2005) Multiple views of the response of an ensemble of spectro-temporal features support concurrent classification of utterance, prosody, sex and speaker identity. Network: Computation in Neural Systems 16:2-3, pages 285-300.
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Nicolas Brunel, Francesco Carusi & Stefano Fusi. (1998) Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. Network: Computation in Neural Systems 9:1, pages 123-152.
Read now
Daniel J Amit & Nicolas Brunel. (1995) Learning internal representations in an attractor neural network with analogue neurons. Network: Computation in Neural Systems 6:3, pages 359-388.
Read now
P C Bressloff. (1995) Stochastic dynamics of reinforcement learning. Network: Computation in Neural Systems 6:2, pages 289-307.
Read now
Davide Badoni, Stefano Bertazzoni, Stefano Buglioni, Gaetano Salina, Daniel J Amit & Stefano Fusi. (1995) Electronic implementation of an analogue attractor neural network with stochastic learning. Network: Computation in Neural Systems 6:2, pages 125-157.
Read now
Michał Żochowski, Maciej Lewenstein & Andrzej Nowak. (1995) SMARTNET: a neural net with self-controlled learning. Network: Computation in Neural Systems 6:1, pages 93-101.
Read now
Daniel J Amit & Nicolas Brunel. (1993) Adequate input for learning in attractor neural networks. Network: Computation in Neural Systems 4:2, pages 177-194.
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Articles from other publishers (22)

Pan Ye Li & Alex Roxin. (2023) Rapid memory encoding in a recurrent network model with behavioral time scale synaptic plasticity. PLOS Computational Biology 19:8, pages e1011139.
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Ulises Pereira-Obilinovic, Johnatan Aljadeff & Nicolas Brunel. (2023) Forgetting Leads to Chaos in Attractor Networks. Physical Review X 13:1.
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Yu Feng & Nicolas Brunel. (2022) Storage capacity of networks with discrete synapses and sparsely encoded memories. Physical Review E 105:5.
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Marco Benedetti, Victor Dotsenko, Giulia Fischetti, Enzo Marinari & Gleb Oshanin. (2021) Recognition capabilities of a Hopfield model with auxiliary hidden neurons. Physical Review E 103:6.
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Yu Liu, Sai Sourabh Yenamachintala & Peng Li. (2019) Energy-efficient FPGA Spiking Neural Accelerators with Supervised and Unsupervised Spike-timing-dependent-Plasticity. ACM Journal on Emerging Technologies in Computing Systems 15:3, pages 1-19.
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Conor Dempsey, LF Abbott & Nathaniel B Sawtell. (2019) Generalization of learned responses in the mormyrid electrosensory lobe. eLife 8.
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Natalia B. Janson & Christopher J. Marsden. (2017) Dynamical system with plastic self-organized velocity field as an alternative conceptual model of a cognitive system. Scientific Reports 7:1.
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TD Barbara Nguyen-Vu, Grace Q Zhao, Subhaneil Lahiri, Rhea R Kimpo, Hanmi Lee, Surya Ganguli, Carla J Shatz & Jennifer L Raymond. (2017) A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity. eLife 6.
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Patrick Kaifosh & Attila Losonczy. (2016) Mnemonic Functions for Nonlinear Dendritic Integration in Hippocampal Pyramidal Circuits. Neuron 90:3, pages 622-634.
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D. J. Willshaw, P. Dayan & R. G. M. Morris. (2015) Memory, modelling and Marr: a commentary on Marr (1971) ‘Simple memory: a theory of archicortex’. Philosophical Transactions of the Royal Society B: Biological Sciences 370:1666, pages 20140383.
Crossref
Carlo BaldassiAlfredo BraunsteinNicolas Brunel & Riccardo Zecchina. (2007) Efficient supervised learning in networks with binary synapses. Proceedings of the National Academy of Sciences 104:26, pages 11079-11084.
Crossref
Stefano Fusi & L F Abbott. (2007) Limits on the memory storage capacity of bounded synapses. Nature Neuroscience 10:4, pages 485-493.
Crossref
Stefano Fusi & Walter Senn. (2006) Eluding oblivion with smart stochastic selection of synaptic updates. Chaos: An Interdisciplinary Journal of Nonlinear Science 16:2.
Crossref
Alfredo Braunstein & Riccardo Zecchina. (2006) Learning by Message Passing in Networks of Discrete Synapses. Physical Review Letters 96:3.
Crossref
S A Vakulenko. (2002) Computational capacity of time-recurrent networks. Journal of Physics A: Mathematical and General 35:11, pages 2539-2554.
Crossref
Nicolas Brunel, Francesco Carusi & Stefano Fusi. (1998) Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. Network: Computation in Neural Systems 9:1, pages 123-152.
Crossref
Daniel Amit†Nicolas Brunel. (1995) Learning internal representations in an attractor neural network with analogue neurons. Network: Computation in Neural Systems 6:3, pages 359-388.
Crossref
P Bressloff. (1995) Stochastic dynamics of reinforcement learning. Network: Computation in Neural Systems 6:2, pages 289-307.
Crossref
Davide Badoni, Stefano Bertazzoni, Stefano Buglioni, Gaetano Salina, Daniel Amit & Stefano Fusi. (1995) Electronic implementation of an analogue attractor neural network with stochastic learning. Network: Computation in Neural Systems 6:2, pages 125-157.
Crossref
Michał Żochowski, Maciej Lewenstein & Andrzej Nowak. (1995) SMARTNET: a neural net with self-controlled learning. Network: Computation in Neural Systems 6:1, pages 93-101.
Crossref
Daniel Amit & Nicolas Brunel. (1993) Adequate input for learning in attractor neural networks. Network: Computation in Neural Systems 4:2, pages 177-194.
Crossref
Daniel J. Amit & Stefano Fusi. 1993. ICANN ’93. ICANN ’93 730 733 .

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