A major challenge in studying sensory processing is to understand the meanings of the neural messages encrypted in the spiking activity of neurons. The ability to """"""""read the neural code"""""""" is essential for our understanding of the neural mechanisms underlying brain functions. The proposed project aims to understand the neural code in the early visual pathway, i.e., the lateral geniculate nucleus (LGN) and the primary visual cortex. We will use a combination of experimental and computational approaches to address this problem from two directions. In the forward direction, we will use a computational method to characterize more systematically features of visual inputs that are encoded in neuronal responses. In the reverse direction, we will reconstruct visual inputs from the recorded neuronal activity. In part 1, we will use decoding techniques to reconstruct spatiotemporal natural scenes from ensemble responses in the LGN. In part 1, we will use decoding techniques to reconstruct spatiotemporal natural scenes from ensemble response in the LGN. The optimal linear decoding technique, which has been used successfully in a preliminary study, will be applied to further explore the functions of precisely correlated spiking in the LGN in visual coding. We will also explore the use of a gradient descent learning algorithm to train artificial neural networks to perform optimal input reconstruction. In part 2, we will use a covariance matrix analysis to systematically characterize the features of visual inputs that are represented by the responses of primary visual cortical neurons. Since most of the cortical neurons are complex cells with highly non-linear responses, a complete description of their coding properties is difficult to obtain with conventional methods. The covariance matrix analysis may help to reveal previously unknown coding properties and may provide new insights into the nature of the neural code in the primary visual cortex. Finally, in part 3, we will apply decoding techniques to reconstruct natural scenes using properties identified by both conventional techniques and by the covariance matrix analysis. This will provide a critical test of our computational model of cortical visual coding developed in part 2. The large amount of information on the anatomy and physiology of the early visual pathway makes it an idea model for computational analysis for neural coding. The results from the proposed studies are likely to provide new insights into the general principles of sensory coding and may advance our understanding of the neuronal mechanisms of higher brain functions under normal and pathological conditions.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
1R01EY012561-01A1
Application #
6041361
Study Section
Special Emphasis Panel (ZRG1-IFCN-8 (01))
Program Officer
Oberdorfer, Michael
Project Start
2000-02-04
Project End
2005-01-31
Budget Start
2000-02-04
Budget End
2001-01-31
Support Year
1
Fiscal Year
2000
Total Cost
$188,762
Indirect Cost
Name
University of California Berkeley
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
094878337
City
Berkeley
State
CA
Country
United States
Zip Code
94704
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Touryan, Jon; Felsen, Gidon; Dan, Yang (2005) Spatial structure of complex cell receptive fields measured with natural images. Neuron 45:781-91
Felsen, Gidon; Touryan, Jon; Dan, Yang (2005) Contextual modulation of orientation tuning contributes to efficient processing of natural stimuli. Network 16:139-49
Felsen, Gidon; Touryan, Jon; Han, Feng et al. (2005) Cortical sensitivity to visual features in natural scenes. PLoS Biol 3:e342
Fu, Yu-Xi; Shen, Yaosong; Gao, Hongfeng et al. (2004) Asymmetry in visual cortical circuits underlying motion-induced perceptual mislocalization. J Neurosci 24:2165-71
Lau, Brian; Stanley, Garrett B; Dan, Yang (2002) Computational subunits of visual cortical neurons revealed by artificial neural networks. Proc Natl Acad Sci U S A 99:8974-9
Touryan, Jon; Lau, Brian; Dan, Yang (2002) Isolation of relevant visual features from random stimuli for cortical complex cells. J Neurosci 22:10811-8
Felsen, Gidon; Shen, Yao-song; Yao, Haishan et al. (2002) Dynamic modification of cortical orientation tuning mediated by recurrent connections. Neuron 36:945-54
Touryan, J; Dan, Y (2001) Analysis of sensory coding with complex stimuli. Curr Opin Neurobiol 11:443-8