Title :
Contextual Categorization by Lateral Inhibition
Author_Institution :
Dept. of Anatomy and Medical Science Program Albert Einstein College of Medicine Bronx, N.Y. 10461
fDate :
7/1/1968 12:00:00 AM
Abstract :
A mechanism is proposed whereby categorization of stimuli is performed on the basis of the context in which such stimuli occur. This mechanism depends on lateral inhibition, which has been experimentally shown to operate in a number of biological receptor systems. It is assumed that n areas in the brain correspond to n classes into which a stimulus might be classified, the category chosen corresponding to the area of maximal excitation. Establishment of mutually inhibitory connections between these areas, such that inhibition follows the equation for lateral inhibition and such that areas corresponding to "like" classes inhibit each other less than areas corresponding to "unlike" classes, leads to contextual categorization.
Keywords :
Computational modeling; Computer simulation; Context modeling; Distributed computing; Equations; Gaussian distribution; Mathematics; Random sequences; Random variables; Stochastic systems;
Journal_Title :
Systems Science and Cybernetics, IEEE Transactions on
DOI :
10.1109/TSSC.1968.300149