Recurrent networks: supervised learning
The handbook of brain theory and neural networks
Dynamic Neural Field Theory for Motion Perception
Dynamic Neural Field Theory for Motion Perception
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Neural Networks - 2005 Special issue: IJCNN 2005
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We propose evolutionary "analysis by synthesis" as a powerful tool in computational neuroscience. We present applications of evolution strategies to the adaptation of dynamical systems for brain modeling. First, we compare evolutionary and gradient-based optimization of dynamic neural fields on an artificial benchmark problem. Then we adjust a few-neuron model developed for explaining our recent findings in a neurobiological experiment, in which we studied the processing of temporal sequences of stimuli in the cortex.