A neuron-like network with the ability to learn coordinated movement patterns.
A model calculation is presented simulating the coordinated interaction between the walking legs of a multi-legged animal. The neural network consists of separate modules with oscillatory capabilities. It has the ability to adjust the necessary parameters for producing a coordinated interaction between the modules in a self-organizing fashion. Some sort of reinforcement comparison learning is used to train the network. It starts oscillations in a completely uncoupled state. After about 100 learning steps, the generation of a stable alternating pattern is usually terminated. Then, the network is able to maintain synchronization, even when disturbances are applied to single agents or to the network as a whole.
Müller-Wilm
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U
Abteilung für Biologische Kybernetik, Universität Bielefeld, Germany.
D000818:Animals; D003400:Astacoidea / Q000502:physiology; D003491:Cybernetics; D005121:Extremities / Q000502:physiology; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D013569:Synapses / Q000502:physiology