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 the promising performance of the approach, which can be turned into a local, and thus "biologically plausible", algorithm.
Cite
@article{arxiv.1402.3563,
title = {Ideomotor feedback control in a recurrent neural network},
author = {Mathieu Galtier},
journal= {arXiv preprint arXiv:1402.3563},
year = {2015}
}