568 related articles for article (PubMed ID: 21395440)
1. 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]
2. Learning only when necessary: better memories of correlated patterns in networks with bounded synapses.
Senn W; Fusi S
Neural Comput; 2005 Oct; 17(10):2106-38. PubMed ID: 16105220
[TBL] [Abstract][Full Text] [Related]
3. Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory.
Sacramento J; Wichert A
Biol Cybern; 2012 Feb; 106(2):123-33. PubMed ID: 22481645
[TBL] [Abstract][Full Text] [Related]
4. A learning rule for very simple universal approximators consisting of a single layer of perceptrons.
Auer P; Burgsteiner H; Maass W
Neural Netw; 2008 Jun; 21(5):786-95. PubMed ID: 18249524
[TBL] [Abstract][Full Text] [Related]
5. Efficient Associative Computation with Discrete Synapses.
Knoblauch A
Neural Comput; 2016 Jan; 28(1):118-86. PubMed ID: 26599711
[TBL] [Abstract][Full Text] [Related]
6. Memory capacities for synaptic and structural plasticity.
Knoblauch A; Palm G; Sommer FT
Neural Comput; 2010 Feb; 22(2):289-341. PubMed ID: 19925281
[TBL] [Abstract][Full Text] [Related]
7. The restricted influence of sparseness of coding on the capacity of familiarity discrimination networks.
Bogacz R; Brown MW
Network; 2002 Nov; 13(4):457-85. PubMed ID: 12463340
[TBL] [Abstract][Full Text] [Related]
8. Bayesian spiking neurons II: learning.
Deneve S
Neural Comput; 2008 Jan; 20(1):118-45. PubMed ID: 18045003
[TBL] [Abstract][Full Text] [Related]
9. Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell.
Brunel N; Hakim V; Isope P; Nadal JP; Barbour B
Neuron; 2004 Sep; 43(5):745-57. PubMed ID: 15339654
[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. Tree-like hierarchical associative memory structures.
Sacramento J; Wichert A
Neural Netw; 2011 Mar; 24(2):143-7. PubMed ID: 20970304
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Memory dynamics in attractor networks with saliency weights.
Tang H; Li H; Yan R
Neural Comput; 2010 Jul; 22(7):1899-926. PubMed ID: 20235821
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Memory capacity of balanced networks.
Aviel Y; Horn D; Abeles M
Neural Comput; 2005 Mar; 17(3):691-713. PubMed ID: 15802011
[TBL] [Abstract][Full Text] [Related]
16. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.
Siri B; Berry H; Cessac B; Delord B; Quoy M
Neural Comput; 2008 Dec; 20(12):2937-66. PubMed ID: 18624656
[TBL] [Abstract][Full Text] [Related]
17. Network effects of synaptic modifications.
Liljenström H
Pharmacopsychiatry; 2010 May; 43 Suppl 1():S67-81. PubMed ID: 20486052
[TBL] [Abstract][Full Text] [Related]
18. Learning of oscillatory correlated patterns in a cortical network by a STDP-based learning rule.
Marinaro M; Scarpetta S; Yoshioka M
Math Biosci; 2007 Jun; 207(2):322-35. PubMed ID: 17306840
[TBL] [Abstract][Full Text] [Related]
19. Input-dependent learning rule for the memory of spatiotemporal sequences in hippocampal network with theta phase precession.
Wu Z; Yamaguchi Y
Biol Cybern; 2004 Feb; 90(2):113-24. PubMed ID: 14999478
[TBL] [Abstract][Full Text] [Related]
20. A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alemi A; Baldassi C; Brunel N; Zecchina R
PLoS Comput Biol; 2015 Aug; 11(8):e1004439. PubMed ID: 26291608
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
