Understanding visual processing of motion: completing the picture using experimentally driven computational models of MT-Reference-Cited by-同舟云学术

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

Published:2023-09-20 Issue:0 Volume:0 Page:
ISSN:0334-1763
Container-title:Reviews in the Neurosciences
language:en
Short-container-title:

Author:

Zarei Eskikand Parvin¹,Grayden David B.¹,Kameneva Tatiana¹²,Burkitt Anthony N.¹,Ibbotson Michael R.³

Affiliation:

1. Department of Biomedical Engineering , The University of Melbourne , Parkville 3052 , Australia

2. Faculty of Science, Engineering and Technology , Swinburne University of Technology , 3122 Hawthorn , Australia

3. National Vision Research Institute , Australian College of Optometry , Carlton 3053 , Australia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

General Neuroscience

Link

https://www.degruyter.com/document/doi/10.1515/revneuro-2023-0052/pdf

Reference60 articles.

1. Adelson, E.H. and Bergen, J.R. (1985). Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2: 284–299, https://doi.org/10.1364/josaa.2.000284.

2. Adelson, E.H. and Movshon, J.A. (1982). Phenomenal coherence of moving visual patterns. Nature 300: 523–525, https://doi.org/10.1038/300523a0.

3. Almasi, A., Meffin, H., Cloherty, S. L., Wong, Y., Yunzab, M., and Ibbotson, M.R. (2020). Mechanisms of feature selectivity and invariance in primary visual cortex. Cerebr. Cortex 30: 5067–5087, https://doi.org/10.1093/cercor/bhaa102.

4. Bayerl, P. and Neumann, H. (2004). Disambiguating visual motion through contextual feedback modulation. Neural Comput. 16: 2041–2066, https://doi.org/10.1162/0899766041732404.

5. Beck, C. and Neumann, H. (2011). Combining feature selection and integration—a neural model for MT motion selectivity. PLoS One 6: e21254, https://doi.org/10.1371/journal.pone.0021254.