Computing with Neural Maps: Application to Perceptual and Cognitive Function
Abstract
In this report, a series of studies concerning the use of neuronal map data structures for the solution of perceptual, attentional and pattern classification problems have been developed. Models for visual attention, based on the representation of an attentional space as a two dimensional map have led to a model of visual attention which has been successfully used in the application of a space-variant active vision system, described below. It has been demonstrated that stereo fusion limits, such as Panum's fusional area, scale in a manner which is determined by the size of a cortical hypercolumn, and the local value of cortical magnification factor, supporting a model in which stereo disparity is computed by a local correlational operator defined on the span of a single pair of ocular dominance columns. Methods for numerically modeling conformal topographic cortical maps have led to important insights into the pattern level description of these cortical systems. A prototype space- variant active vision system has been constructed, with funds for hardware support from DARPA, and a number of difficult algorithmic problems in motor control, attention, space-variant image processing, and space-variant pattern classification, have begun to studied. One book has been published in this project period: Computational Neuroscience, Eric Schwartz, MIT Press (1990) which presented the proceedings of an earlier conference which introduced the term 'Computational Neuroscience' into its current widespread use.... Visual cortex, Vision, Pattern recognition, Active vision.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 26, 1993
- Accession Number
- ADA264092
Entities
People
- Eric L. Schwartz
Organizations
- NYU Langone Health