HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Supplemental Figures All Versions of this Article: 102/2/953    most recent 90651.2008v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Michler, F. Articles by Wachtler, T. PubMed PubMed Citation Articles by Michler, F. Articles by Wachtler, T.

Using Spatiotemporal Correlations to Learn Topographic Maps for Invariant Object Recognition

NeuroPhysics Group, Philipps-University Marburg, Marburg, Germany

Submitted 9 June 2008; accepted in final form 29 May 2009

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.