203 related articles for article (PubMed ID: 10695764)
1. Effective neural response function for collective population states.
Mascaro M; Amit DJ
Network; 1999 Nov; 10(4):351-73. PubMed ID: 10695764
[TBL] [Abstract][Full Text] [Related]
2. Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories.
Curti E; Mongillo G; La Camera G; Amit DJ
Neural Comput; 2004 Dec; 16(12):2597-637. PubMed ID: 15516275
[TBL] [Abstract][Full Text] [Related]
3. Sparseness constrains the prolongation of memory lifetime via synaptic metaplasticity.
Leibold C; Kempter R
Cereb Cortex; 2008 Jan; 18(1):67-77. PubMed ID: 17490993
[TBL] [Abstract][Full Text] [Related]
4. Advancing the boundaries of high-connectivity network simulation with distributed computing.
Morrison A; Mehring C; Geisel T; Aertsen AD; Diesmann M
Neural Comput; 2005 Aug; 17(8):1776-801. PubMed ID: 15969917
[TBL] [Abstract][Full Text] [Related]
5. A working memory model based on fast Hebbian learning.
Sandberg A; Tegnér J; Lansner A
Network; 2003 Nov; 14(4):789-802. PubMed ID: 14653503
[TBL] [Abstract][Full Text] [Related]
6. Background-activity-dependent properties of a network model for working memory that incorporates cellular bistability.
Fall CP; Lewis TJ; Rinzel J
Biol Cybern; 2005 Aug; 93(2):109-18. PubMed ID: 15806392
[TBL] [Abstract][Full Text] [Related]
7. "What" and "where" in visual working memory: a computational neurodynamical perspective for integrating FMRI and single-neuron data.
Deco G; Rolls ET; Horwitz B
J Cogn Neurosci; 2004 May; 16(4):683-701. PubMed ID: 15165356
[TBL] [Abstract][Full Text] [Related]
8. Spike-driven synaptic dynamics generating working memory states.
Amit DJ; Mongillo G
Neural Comput; 2003 Mar; 15(3):565-96. PubMed ID: 12620158
[TBL] [Abstract][Full Text] [Related]
9. Learning in realistic networks of spiking neurons and spike-driven plastic synapses.
Mongillo G; Curti E; Romani S; Amit DJ
Eur J Neurosci; 2005 Jun; 21(11):3143-60. PubMed ID: 15978023
[TBL] [Abstract][Full Text] [Related]
10. Optimizing one-shot learning with binary synapses.
Romani S; Amit DJ; Amit Y
Neural Comput; 2008 Aug; 20(8):1928-50. PubMed ID: 18386988
[TBL] [Abstract][Full Text] [Related]
11. A learning rule for the emergence of stable dynamics and timing in recurrent networks.
Buonomano DV
J Neurophysiol; 2005 Oct; 94(4):2275-83. PubMed ID: 16160088
[TBL] [Abstract][Full Text] [Related]
12. Computing with continuous attractors: stability and online aspects.
Wu S; Amari S
Neural Comput; 2005 Oct; 17(10):2215-39. PubMed ID: 16105223
[TBL] [Abstract][Full Text] [Related]
13. Correlation between eigenvalue spectra and dynamics of neural networks.
Zhou Q; Jin T; Zhao H
Neural Comput; 2009 Oct; 21(10):2931-41. PubMed ID: 19635013
[TBL] [Abstract][Full Text] [Related]
14. Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis.
Shimansky YP
Biol Cybern; 2009 Dec; 101(5-6):379-85. PubMed ID: 19844738
[TBL] [Abstract][Full Text] [Related]
15. Inferring network activity from synaptic noise.
Rudolph M; Destexhe A
J Physiol Paris; 2004; 98(4-6):452-66. PubMed ID: 16289550
[TBL] [Abstract][Full Text] [Related]
16. Dynamics and robustness of familiarity memory.
Cortes JM; Greve A; Barrett AB; van Rossum MC
Neural Comput; 2010 Feb; 22(2):448-66. PubMed ID: 19842985
[TBL] [Abstract][Full Text] [Related]
17. Eluding oblivion with smart stochastic selection of synaptic updates.
Fusi S; Senn W
Chaos; 2006 Jun; 16(2):026112. PubMed ID: 16822044
[TBL] [Abstract][Full Text] [Related]
18. Computational and in vitro studies of persistent activity: edging towards cellular and synaptic mechanisms of working memory.
Compte A
Neuroscience; 2006 Apr; 139(1):135-51. PubMed ID: 16337341
[TBL] [Abstract][Full Text] [Related]
19. Synaptic theory of working memory.
Mongillo G; Barak O; Tsodyks M
Science; 2008 Mar; 319(5869):1543-6. PubMed ID: 18339943
[TBL] [Abstract][Full Text] [Related]
20. Neural associative memory with optimal Bayesian learning.
Knoblauch A
Neural Comput; 2011 Jun; 23(6):1393-451. PubMed ID: 21395440
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
