Vision is active. It is a dynamic process, resulting from an interaction between context, perceptual learning and top-down influences. All cortical and thalamic levels of sensory processing are subject to powerful top-down influences, the shaping of lower level processes by higher order, more complex information and cognitive states. There is an expanding view of the kind of information that is conveyed in a top-down fashion, including attention, expectation, and perceptual task. As a consequence every cortical area acts as an adaptive processor, undergoing continuing cycles of state change and functional switching, with earlier states influencing the way in which the bottom up sensory information is interpreted in subsequent states. In this proposal we will explore the functional role of top-down influences, study the mechanisms by which these influences are exerted, and determine how different visual cortical areas participate in top-down interactions. We will continue our studies on perceptual learning, determining how learning object shape affects the functional properties of neurons at different stages in the visual pathway. We will study the role of early visual areas in intermediate level vision, the parsing of visual images into objects and background, contour integration, and the representation of object shape. The experimental approach will involve recording from single neurons and neuronal ensembles while animals are performing visual discrimination tasks, which enables us to determine how learning and perceptual task influences the function of different cortical areas, the circuit mechanism of this process, and the way in which these areas analyze visual images.

Public Health Relevance

The mechanisms of visual processing studied in this proposal are likely to be general to the brain as a whole. It will help us understand how perceptual learning facilitates our ability to interpret visual scenes, and to understand how dysfunction of experience dependent change and top-down influences plays a role in visual disorders such as amblyopia, and more broadly, behavioral disorders.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY007968-22
Application #
8123265
Study Section
Central Visual Processing Study Section (CVP)
Program Officer
Steinmetz, Michael A
Project Start
1989-08-01
Project End
2013-08-31
Budget Start
2011-09-01
Budget End
2012-08-31
Support Year
22
Fiscal Year
2011
Total Cost
$405,600
Indirect Cost
Name
Rockefeller University
Department
Biology
Type
Other Domestic Higher Education
DUNS #
071037113
City
New York
State
NY
Country
United States
Zip Code
10065
van Kerkoerle, Timo; Marik, Sally A; Meyer Zum Alten Borgloh, Stephan et al. (2018) Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex. Proc Natl Acad Sci U S A 115:10464-10469
Abe, Hiroshi; McManus, Justin N J; Ramalingam, Nirmala et al. (2015) Adult cortical plasticity studied with chronically implanted electrode arrays. J Neurosci 35:2778-90
Piëch, Valentin; Li, Wu; Reeke, George N et al. (2013) Network model of top-down influences on local gain and contextual interactions in visual cortex. Proc Natl Acad Sci U S A 110:E4108-17
Ramalingam, Nirmala; McManus, Justin N J; Li, Wu et al. (2013) Top-down modulation of lateral interactions in visual cortex. J Neurosci 33:1773-89
Gilbert, Charles D; Li, Wu (2013) Top-down influences on visual processing. Nat Rev Neurosci 14:350-63
McManus, Justin N J; Li, Wu; Gilbert, Charles D (2011) Adaptive shape processing in primary visual cortex. Proc Natl Acad Sci U S A 108:9739-46
Gölcü, Doruk; Gilbert, Charles D (2009) Perceptual learning of object shape. J Neurosci 29:13621-9
Kinoshita, Masaharu; Gilbert, Charles D; Das, Aniruddha (2009) Optical imaging of contextual interactions in V1 of the behaving monkey. J Neurophysiol 102:1930-44
Li, Wu; Piech, Valentin; Gilbert, Charles D (2008) Learning to link visual contours. Neuron 57:442-51
McManus, Justin N J; Ullman, Shimon; Gilbert, Charles D (2008) A computational model of perceptual fill-in following retinal degeneration. J Neurophysiol 99:2086-100

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