In:
Journal of Neurophysiology, American Physiological Society, Vol. 102, No. 2 ( 2009-08), p. 953-964
Abstract:
The retinal image of visual objects can vary drastically with changes of viewing angle. Nevertheless, our visual system is capable of recognizing objects fairly invariant of viewing angle. Under natural viewing conditions, different views of the same object tend to occur in temporal proximity, thereby generating temporal correlations in the sequence of retinal images. Such spatial and temporal stimulus correlations can be exploited for learning invariant representations. We propose a biologically plausible mechanism that implements this learning strategy using the principle of self-organizing maps. We developed a network of spiking neurons that uses spatiotemporal correlations in the inputs to map different views of objects onto a topographic representation. After learning, different views of the same object are represented in a connected neighborhood of neurons. Model neurons of a higher processing area that receive unspecific input from a local neighborhood in the map show view-invariant selectivities for visual objects. The findings suggest a functional relevance of cortical topographic maps.
Type of Medium:
Online Resource
ISSN:
0022-3077
,
1522-1598
DOI:
10.1152/jn.90651.2008
Language:
English
Publisher:
American Physiological Society
Publication Date:
2009
detail.hit.zdb_id:
80161-6
detail.hit.zdb_id:
1467889-5
Permalink
