THE CORTICAL COLUMN - A NEW PROCESSING UNIT FOR MULTILAYERED NETWORKS

We propose in this paper a new connectionist unit that matches a biological model of the cortical column. The architectural and functional characteristics of this unit have been designed in the simplest manner in order to simulate human-like reasoning, and to be as similar as possible to the main known features of real intracortical networks. We use a new type of learning rule which can easily take into account goal-oriented combinations of actions in behavioral programs. These learning rules are both simple and biologically plausible. We show in this paper that such units can be used in multilayered networks to perform pattern recognition, with feedback connections effecting an attentive gating of sensory information flow. Computer simulations were performed to assess the ability of a multilayered network made of these biologically inspired units to perform standard speech and visual recognition. Such simulations show levels of performance equivalent to the best currently available connectionist networks for typical human-like problems, with very fast learning and recognition processes. Furthermore, this type of "cortical" unit can be used in more general multilayered networks with units controlling different types of external processing, in order to learn programs of actions which may be included in the process of recognition.