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BIOSYSTEMS
2007
82views more  BIOSYSTEMS 2007»
15 years 6 months ago
Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks
Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node ...
Hywel T. P. Williams, Jason Noble
CONNECTION
2004
98views more  CONNECTION 2004»
15 years 6 months ago
Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting
While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. ...
Bernard Ans, Stephane Rousset, Robert M. French, S...
IJON
2000
76views more  IJON 2000»
15 years 6 months ago
Fast neural network simulations with population density methods
The complexity of neural networks of the brain makes studying these networks through computer simulation challenging. Conventional methods, where one models thousands of individua...
Duane Q. Nykamp, Daniel Tranchina
TNN
2010
182views Management» more  TNN 2010»
15 years 1 months ago
A discrete-time neural network for optimization problems with hybrid constraints
Abstract--Recurrent neural networks have become a prominent tool for optimizations including linear or nonlinear variational inequalities and programming, due to its regular mathem...
Huajin Tang, Haizhou Li, Zhang Yi
ICASSP
2011
IEEE
14 years 10 months ago
Extensions of recurrent neural network language model
We present several modifications of the original recurrent neural network language model (RNN LM). While this model has been shown to significantly outperform many competitive l...
Tomas Mikolov, Stefan Kombrink, Lukas Burget, Jan ...