Behavioral and Brain Sciences



Short Communication

Learning and control with chaos: From biology to robotics


Mathias Quoy a1, Jean-Paul Banquet a2 and Emmanuel Daucé a3
a1 Neurocybernetics Group, ETIS Lab, Université de Cergy-Pontoise, Cergy-Pontoise, 95014, France quoy@u-cergy.fr www-etis.ensea.fr/~quoy/perso.html
a2 Neuroscience et modélisation, Université Pierre et Marie Curie, Paris, 75252, France banquet@ccr.jussieu.fr
a3 CERT-ONERA-DTIM, Toulouse, 31400, France dauce@cert.fr www.cert.fr/anglais/deri/dauce

Abstract

After critical appraisal of mathematical and biological characteristics of the model, we discuss how a classical hippocampal neural network expresses functions similar to those of the chaotic model, and then present an alternative stimulus-driven chaotic random recurrent neural network (RRNN) that learns patterns as well as sequences, and controls the navigation of a mobile robot.