The aim of this project is to elucidate the strategies of information processing by the visual cortex. Anatomical findings suggest that there are common patterns of connectivity within and between different cortical areas, and therefore similar neural mechanisms underlying the transformation of receptive field structure occurring in each area. By relating the connectivity and functional properties of cells in each of several visual areas one can gain insight into the processing mechanisms that are general to the cortex as a whole. The main focus of this study will be the pathway involved in the analysis of form. The approach will be to delineate the connections of this pathway, to relate patterns of connectivity to the functional architecture of cortex, and to determine the transformation of receptive field properties at successive stages in the pathway. By using complex stimuli, we will analyze the receptive field properties of cells along this pathway, and will determine how the response properties vary according to the context in which the stimulus is presented. Several possible anatomical substrates for the response properties of cells will be investigated. In particular, the role of horizontal connections, represented by long range intrinsic connections running parallel to the cortical surface and converging forward and feedback cortico-cortical projections, will be studied for their role in integrating information across the visual field. Having found properties that may be related to a known connection, we will block the activity of the source of that connection to demonstrate its role in generating those properties. These experiments will help explain how information coming from different parts of the visual field is integrated into a unified percept. The ultimate objective is to relate our observations on the responses of single cells or cell ensembles to psychophysical observations on the contextual influences in the analysis of form. This knowledge will aid in localizing cortical lesions that lead to specific derangements of vision, and in understanding the mechanisms underlying diseases of the nervous system affecting the cortex.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY007968-02
Application #
3265044
Study Section
Visual Sciences B Study Section (VISB)
Project Start
1989-08-01
Project End
1994-07-31
Budget Start
1990-08-01
Budget End
1991-07-31
Support Year
2
Fiscal Year
1990
Total Cost
Indirect Cost
Name
Rockefeller University
Department
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

Showing the most recent 10 out of 36 publications