The value of lateral connectivity in visual cortex for interpreting naturalistic images
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1
Columbia University, Center for Theoretical Neuroscience, United States
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2
Columbia University, Department of Statistics, United States
In many species, orientation-selective neurons in primary visual cortex are preferentially connected to others with similar orientation selectivity. It has been hypothesized that this circuitry is tuned to the statistics of natural scenes, and helps refine the interpretation of feedforward visual inputs: it may provide a mechanism by which neural networks can exploit the natural correlations between nearby edges. A related but more general claim holds for the machine learning algorithm called belief propagation. In this algorithm, model neurons encode the probabilities of local features and send information about their current state to other neurons encoding statistically related features. Following these dynamics, a network of such neurons converges to a consensus state representing the most probable synthesis of the available information. In this study we test how well neurally plausible approximations of belief propagation perform on feature discrimination tasks. Using a simple occlusion model of natural scenes, we are able to compute the probabilities of image features exactly, providing a ground truth against which we compare the performance of these model networks. We calculate how performance varies with the specificity and range of lateral connectivity, and compare the effectiveness of a given connectivity structure for neurons of different response types (simple cells, complex cells, border ownership cells). Finally we discuss the implications of these results for neural coding in primary and secondary visual cortex.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session II
Citation:
Pitkow
X,
Ahmadian
Y and
Miller
K
(2010). The value of lateral connectivity in visual cortex for interpreting naturalistic images.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00209
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Received:
04 Mar 2010;
Published Online:
04 Mar 2010.
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Correspondence:
Xaq Pitkow, Columbia University, Center for Theoretical Neuroscience, New York, United States, xaq@post.harvard.edu