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COMPLEXITY
2008
84views more  COMPLEXITY 2008»
13 years 6 months ago
Evolutionary learning of small networks
Results are presented of a simulation which mimics an evolutionary learning process for small networks. Special features of these networks include a high recurrency, a transition ...
Thomas Filk, Albrecht von Müller
IROS
2009
IEEE
201views Robotics» more  IROS 2009»
14 years 28 days ago
Modeling tool-body assimilation using second-order Recurrent Neural Network
— Tool-body assimilation is one of the intelligent human abilities. Through trial and experience, humans are capable of using tools as if they are part of their own bodies. This ...
Shun Nishide, Tatsuhiro Nakagawa, Tetsuya Ogata, J...
BC
2002
193views more  BC 2002»
13 years 6 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
ESANN
2007
13 years 7 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
GECCO
2005
Springer
204views Optimization» more  GECCO 2005»
13 years 11 months ago
Modeling systems with internal state using evolino
Existing Recurrent Neural Networks (RNNs) are limited in their ability to model dynamical systems with nonlinearities and hidden internal states. Here we use our general framework...
Daan Wierstra, Faustino J. Gomez, Jürgen Schm...