NDLI: Neural Mechanisms Underlying Visual Object Recognition.

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Neural Mechanisms Underlying Visual Object Recognition.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Afraz, Arash Yamins, Daniel L. K. DiCarlo, James J.
Description	Author Affiliation: Afraz A ( Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.); Yamins DL ( Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.); DiCarlo JJ ( Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 dicarlo@mit.edu.)
Abstract	Invariant visual object recognition and the underlying neural representations are fundamental to higher-level human cognition. To understand these neural underpinnings, we combine human and monkey psychophysics, large-scale neurophysiology, neural perturbation methods, and computational modeling to construct falsifiable, predictive models that aim to fully account for the neural encoding and decoding processes that underlie visual object recognition. A predictive encoding model must minimally describe the transformation of the retinal image to population patterns of neural activity along the entire cortical ventral stream of visual processing and must accurately predict the responses to any retinal image. A predictive decoding model must minimally describe the transformation from those population patterns of neural activity to observed object recognition behavior (i.e., subject reports), and, given that population pattern of activity, it must accurately predict behavior for any object recognition task. To date, we have focused on core object recognition-a remarkable behavior that is accomplished with image viewing durations of <200 msec. Our work thus far reveals that the neural encoding process is reasonably well explained by a largely feed-forward, highly complex, multistaged nonlinear neural network-the current best neuronal simulation models predict approximately one-half of the relevant neuronal response variance across the highest levels of the ventral stream (areas V4 and IT). Remarkably, however, the decoding process from IT to behavior for all object recognition tasks tested thus far is very accurately predicted by simple direct linear conversion of the inferior temporal neural population state to behavior choice. We have recently examined the behavioral consequences of direct suppression of IT neural activity using pharmacological and optogenetic methods and find them to be well-explained by the same linear decoding model.
File Format	HTM / HTML
ISSN	00917451
e-ISSN	19434456
Journal	Cold Spring Harbor Symposia on Quantitative Biology
Volume Number	79
Language	English
Publisher	Cold Spring Harbor Laboratory Press
Publisher Date	2014-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Biology Neural Pathways Physiology Pattern Recognition, Visual Animals Models, Neurological Neurons
Content Type	Text
Resource Type	Article
Subject	Genetics Biochemistry Molecular Biology

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