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arXiv · 2305.09317

Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT

Parvin Zarei Eskikand·David B Grayden·Tatiana Kameneva·Anthony N Burkitt·Michael R Ibbotson
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Abstract

Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural computation. Computational modeling of the neuronal pathways of the visual cortex has been successful in developing theories of biological motion processing. This review describes a range of computational models that have been inspired by neurophysiological experiments. Theories of local motion integration and pattern motion processing are presented, together with suggested neurophysiological experiments designed to test those hypotheses.

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Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva, Anthony N Burkitt, Michael R Ibbotson. 2023-05-16. Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT. https://arxiv.org/abs/2305.09317

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