August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Modern population approaches for discovering neural representations and for discriminating among algorithms that might produce those representations.
Author Affiliations
  • James J. DiCarlo, MD, PhD
    Professor of Neuroscience Head, Department of Brain and Cognitive Sciences Investigator, McGovern Institute for Brain Research Massachusetts Institute of Technology, Cambridge, USA
    Speaker
  • Ha Hong
    Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research Massachusetts Institute of Technology, Cambridge, USA
    Author
  • Daniel Yamins
    Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research Massachusetts Institute of Technology, Cambridge, USA
    Author
Journal of Vision August 2014, Vol.14, 1477. doi:10.1167/14.10.1477
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      James J. DiCarlo, MD, PhD, Ha Hong, Daniel Yamins; Modern population approaches for discovering neural representations and for discriminating among algorithms that might produce those representations.. Journal of Vision 2014;14(10):1477. doi: 10.1167/14.10.1477.

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

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Abstract

Visual object recognition (OR) is a central problem in systems neuroscience, human psychophysics, and computer vision. The primate ventral stream, which culminates in inferior temporal cortex (IT), is an instantiation of a powerful OR system. To understand this system, our approach is to first drive a wedge into the problem by finding the specific patterns of neuronal activity (a.k.a. neural "representations") that quantitatively express the brainÂ’s solution to OR. I will argue that, to claim discovery of a neural "representation" for OR, one must show that a proposed population of visual neurons can perfectly predict psychophysical phenomena pertaining to OR. Using simple decoder tools, we have achieved exactly this result, demonstrating that IT representations (as opposed to V4 representations) indeed predict OR phenomena. Moreover, we can "invert" the decoder approach to use large-scale psychophysical measurements to make new, testable predictions about the IT representation. While decoding methods are powerful for exploring the link between neural activity and behavior, they are less well suited for addressing how pixel representations (i.e. images) are transformed into neural representations that subserve OR. To address this issue, we have adopted the representational dissimilarity matrices (RDM) approach promoted by Niko Kriegeskorte. We have recently discovered novel models (i.e. image-computable visual features) that, using the RDM measure of success, explain IT representations dramatically better than all previous models.

Meeting abstract presented at VSS 2014

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