A neural paradigm for controlling autonomous systems with reflex behaviour and learning capability

Abstract

In this paper we present a neural paradigm for controlling the reflex behaviour of autonomous systems which are able to modify their behaviour by interaction with the environment. This paradigm incorporates the ideas expressed by Russell [1] about how to model the living being's reflex behaviour. In this paradigm a new type of connection is introduced: the so called high order Or connection. Learning is local and unsupervised, i.e., the change in the weight of a connection takes place as a consequence of its activation. We present two functions to update the weights which incorporate the forgetting capability. Some topologies have been simulated to provide the basic capabilities such as inhibition, stimuli association an reinforcement.