August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Testing the binocular energy model with response variability
Author Affiliations
  • Sid Henriksen
    Laboratory of Sensorimotor Research, National Institutes of Health
  • Jenny Read
    Institute of Neuroscience, Newcastle University
  • Bruce Cumming
    Laboratory of Sensorimotor Research, National Institutes of Health
Journal of Vision September 2016, Vol.16, 437. doi:10.1167/16.12.437
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      Sid Henriksen, Jenny Read, Bruce Cumming; Testing the binocular energy model with response variability. Journal of Vision 2016;16(12):437. doi: 10.1167/16.12.437.

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

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Abstract

The binocular energy model (BEM) has proven to be a very successful description of disparity selective neurons in area V1. However, most tests of the model compare mean responses to different stimuli - little attention has been given to the distribution of responses predicted by the BEM. To explore the distribution of responses, we computed the responses of BEM units to dynamic 1D noise stimuli at a range of disparities, with either positive or negative binocular correlation. Each frame was presented for 30 ms. The distribution of the model's responses to these stimuli was highly kurtotic, similar to an exponential distribution. As for exponential distributions, the variance grows with the square of the mean, meaning that the Fano factor (variance/mean) in the model is proportional to the mean. This is in stark contrast to cells in V1, where the Fano factor depends only weakly on the mean. This means that disparity-related changes in the mean response of linear-nonlinear models are largely caused by a small number of frames which elicit very large responses, rather than, for example, reflecting a change in the mean of a Gaussian distribution. We show that the dependence of Fano factor on disparity holds even for linear-nonlinear cascade models of binocular cells that have been fit to real cells using recently developed optimization techniques. Importantly the neuronal data (to which the models were fit) do not show the same dependence of Fano factor on disparity. We show that it is possible to greatly reduce the Fano factor variation in the model by introducing a form of monocular gain control. We propose that incorporating monocular gain control is critical for adequately modeling the trial-to-trial dynamics of disparity-selective cells in V1.

Meeting abstract presented at VSS 2016

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