Learning and control with large dynamic neural networks

This paper is a presentation of neuronal control systems in the terms of the dynamical systems theory, where (1) the controller and its surrounding environment are seen as two co-dependent controlled dynamical systems (2) the behavioral transitions that take place under adaptation processes are analyzed in terms of phase-transitions. We present in the second section a generic method for the construction of multi-population random recurrent neural networks. The third section gives an overview of the various phase transitions that take place under an external forcing signal, or under internal parametric changes. The section 4 presents some applications in the domain of sequence identification and active perception modeling. The section 5 presents some applications in the domain of closed-loop control systems and reinforcement learning.