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» Intrinsic plasticity for reservoir learning algorithms
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ESANN
2007
13 years 6 months ago
Intrinsic plasticity for reservoir learning algorithms
One of the most difficult problems in using dynamic reservoirs like echo state networks for signal processing is the choice of reservoir network parameters like connectivity or spe...
Marion Wardermann, Jochen J. Steil
ICONIP
2010
13 years 3 months ago
Improving Recurrent Neural Network Performance Using Transfer Entropy
Abstract. Reservoir computing approaches have been successfully applied to a variety of tasks. An inherent problem of these approaches, is, however, their variation in performance ...
Oliver Obst, Joschka Boedecker, Minoru Asada
NECO
2007
258views more  NECO 2007»
13 years 4 months ago
Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spiketiming-dependent plasticity (...
Razvan V. Florian
NN
2006
Springer
140views Neural Networks» more  NN 2006»
13 years 4 months ago
Neural mechanism for stochastic behaviour during a competitive game
Previous studies have shown that non-human primates can generate highly stochastic choice behaviour, especially when this is required during a competitive interaction with another...
Alireza Soltani, Daeyeol Lee, Xiao-Jing Wang