September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Neural circuitry of brightness induction: modeling and physiology
Author Affiliations
  • Hannah Choi
    Departments of Ophthalmology, Physiology and Pharmacology, The Graduate Programs in Neural and Behavioral Science, and Biomedical Engineering, The SUNY Eye Institute, State University of New York Downstate Medical Center Department of Applied Mathematics, University of Washington (Current)
  • Artak Khachatryan
    Departments of Ophthalmology, Physiology and Pharmacology, The Graduate Programs in Neural and Behavioral Science, and Biomedical Engineering, The SUNY Eye Institute, State University of New York Downstate Medical Center
  • Charm Le'Pre
    Departments of Ophthalmology, Physiology and Pharmacology, The Graduate Programs in Neural and Behavioral Science, and Biomedical Engineering, The SUNY Eye Institute, State University of New York Downstate Medical Center
  • Ehud Kaplan
    Friedman Brain Institute Departments of Neuroscience, Ophthalmology, Structural & Chemical Biology, The Icahn School of Medicine at Mount Sinai
  • Qasim Zaidi
    Graduate Center for Vision Research, SUNY College of Optometry
  • Youping Xiao
    Departments of Ophthalmology, Physiology and Pharmacology, The Graduate Programs in Neural and Behavioral Science, and Biomedical Engineering, The SUNY Eye Institute, State University of New York Downstate Medical Center
Journal of Vision September 2015, Vol.15, 638. doi:10.1167/15.12.638
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      Hannah Choi, Artak Khachatryan, Charm Le'Pre, Ehud Kaplan, Qasim Zaidi, Youping Xiao; Neural circuitry of brightness induction: modeling and physiology. Journal of Vision 2015;15(12):638. doi: 10.1167/15.12.638.

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

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Abstract

An important function of the brain is adjusting perception to context, so that estimates of object properties are relatively consistent despite variations in retinal inputs. Brightness induction, including both contrast and assimilation, is a classical example of context effects on surface perception, but the underlying neural circuits are still unknown. We have developed a neural model in which each unit at the cortical site of induction receives inputs from receptive fields (RFs) that are spatially opponent and orientation selective, and also from recently found RFs that are neither spatially opponent nor orientation selective. The synaptic weights for both input types are Gaussian functions of the distance in visual space between the input and recipient RFs. The weights for spatially opponent RFs also depend on orientation preference and contrast polarity. The parameters for the synaptic weight function were adjusted for the model to reproduce contrast-linearity of induction and exponential fall-off in context effects with distance from the test. With these parameters fixed, the model reproduced a large variety of complex contrast and assimilation effects, including Mach bands, grating induction, Helson’s line stimuli showing assimilation versus contrast, Todorovic effects, Bullseye effect and 2-D and 3-D variants of White’s Effect without explicitly extracting image junctions. By simulating deficits in explanatory power when removing specific types of RFs and connections from the model, we predicted specific functional connectivity that can be tested physiologically. With electrode-array recordings in anesthetized macaques, we found many neurons in area V4 that responded in opposite-phase to brightness modulations of the surround, a classical signature of brightness contrast, whereas almost all neurons in V2 responded in the same phase as the surround modulation. Consequently, our preliminary results suggest that V4 is the site where the two types of RFs converge to generate brightness induction.

Meeting abstract presented at VSS 2015

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