Paper Info
Title
The cortical column: a new processing unit for multilayered networks
Abstract
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.
Year
DOI
Venue
1991
10.1016/0893-6080(91)90027-3
Neural Networks
Keywords
Field
DocType
multilayered network,new processing unit,cortical column
Computer science,Biological modeling,Learning rule,Visual recognition,Cortical column,Artificial intelligence,Artificial neural network,Connectionism,Machine learning
Journal
Volume
Issue
ISSN
4
1
Neural Networks
Citations 
PageRank 
References 
17
1.85
2
Authors
4
Name
Order
Citations
PageRank
Frédéric Alexandre18215.94
Frédéric Guyot2212.67
Jean-Paul Haton338065.42
Yves Burnod410216.36