Principles and applications of the cortical column symbolic neural model

Abstract

Presents the basic principles and some preliminary applications of a novel connectionist model for building up a neural network system. The model uses the cortical column instead of the classical neuron as the basic processing unit; it is closely related to the neurobiological modeling of the human cortex. The main features of this model which make it different from classical approaches concern the local connectivity and the integration of time by causality learning. The model provides a basic unit well -adapted to solve humanlike problems by integrating particular difficulties in its own structure (e.g. the coarticulation effect in speech recognition). The model has been applied to two difficult problems in the artificial intelligence field: speech recognition (more precisely, the acoustic-phonetic decoding of continuous speech) and biomedical X-ray image interpretation. The two systems that were designed for these applications demonstrate the ability of the cortical column to solve perceptive and cognitive tasks