September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain
Author Affiliations & Notes
  • Paria Mehrani
    York University
  • John K. Tsotsos
    York University
  • Footnotes
    Acknowledgements  We are grateful to Dr.Anitha Pasupathy for providing V4 responses, and for the support of the following sources:Air Force Office of Scientific Research[FA9550-18-1-0054];the Canada Research Chairs Program[950-231659];the Natural Sciences and Engineering Research Council of Canada[RGPIN-2016-05352].
Journal of Vision September 2021, Vol.21, 1910. doi:https://doi.org/10.1167/jov.21.9.1910
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      Paria Mehrani, John K. Tsotsos; Learning a model of shape selectivity in V4 cells reveals shape encoding mechanisms in the brain. Journal of Vision 2021;21(9):1910. doi: https://doi.org/10.1167/jov.21.9.1910.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The mechanisms of local shape information transformation from V1 to more abstract representations in IT are unknown. Studying the selectivities in intermediate stages of transformation suggest plausible mechanisms. For example, Pasupathy and Conner [1] studied Macaque V4 responses to convexities and concavities. They found that these neurons are selective to boundary configurations at a specific position in the stimulus, for example, a convexity adjacent to a concavity. Although such investigations reveal intermediate shape representations in the brain, they often do not suffice in capturing complex and long-range interactions within the receptive field due to imposing priors on tunings, e.g., fitting a single Gaussian to neuron responses. Here, we propose a learning-based approach that eliminates the need for such strong priors. Specifically, we investigate shape representation in Macaque V4 cells and formulate shape tuning as a sparse-coding problem according to previous findings of V4 neurons[1]. We emphasize that our goal is not to find a mapping from the stimulus to V4 responses but to study how V4 neurons combine responses of curvature-selective V2 cells to achieve their reported part-based selectivities. To this end, our proposed model takes responses of simulated curvature-selective V2 cells as input by combining two previously introduced hierarchical models [2,3]. With simulated curvature signal as input, our algorithm learns a sparse mapping to V4 responses that reveals each Macaque V4 cell’s tuning and the mechanism by which the tuning is achieved. Our model captures sophisticated interactions within the receptive field from neuron responses. Our results on V4 shape representations confirm long-range interactions between components of a larger shape, providing a better understanding of shape encoding mechanisms in the brain. [1] A. Pasupathy et al. J. of Neurophysiology(2001) 86:5, 2505-2519. [2] A. J. Rodríguez-Sánchez et al. PLoS One (2012) 7(8), e42058. [3] P. Mehrani et al. (2019) arXiv:1901.03201.

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