title: Ideomotor feedback control in a recurrent neural network.
abstract: The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.
authors.affiliation: Minds, Jacobs University Bremen, Bremen, Germany, mathieu.galtier@gmx.com.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Galtier
mesh_terms: D000465:Algorithms; D000818:Animals; D004777:Environment; D025461:Feedback, Physiological / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D017711:Nonlinear Dynamics; D010812:Physical Stimulation