Using a delay adaptation learning rule, we model the activity-dependent development of directionally selective cells in the primary visual cortex. Based on input stimuli, a learning rule shifts delays to create synchronous arrival of spikes at cortical cells. As a result, delays become tuned creating a smooth cortical map of direction selectivity. This result demonstrates how delay adaption can serve as a powerful abstraction for modeling temporal learning in the brain.

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Neurocomputing, 44--46:679--684, 2002. Also in J. M. Bower (editor), Computational Neuroscience: Trends in Research, 2002 (CNS*01, Pacific Grove, CA). New York: Elsevier.

@article{tversky:neurocomputing02, title={Modeling Directional Selectivity Using Self-Organizing Delay-Adaptation Maps}, author={Tal Tversky and Risto Miikkulainen}, volume={44--46}, journal={Neurocomputing}, pages={679--684}, note={Also in J. M. Bower (editor), Computational Neuroscience: Trends in Research, 2002 (CNS*01, Pacific Grove, CA). New York: Elsevier}, url="http://nn.cs.utexas.edu/?tversky:cns01", year={2002} }