Inference and segmentation in cortical processing

Yuan Liu; Guillermo A. Cecchi; A. Ravishankar Rao; James Kozloski; Charles C. Peck

doi:10.1117/12.650804

9 February 2006 Inference and segmentation in cortical processing

Yuan Liu, Guillermo A. Cecchi, A. Ravishankar Rao, James Kozloski, Charles C. Peck

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Proceedings Volume 6057, Human Vision and Electronic Imaging XI; 60570Y (2006) https://doi.org/10.1117/12.650804
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

We present a modelling framework for cortical processing aimed at understanding how, maintaining biological plausibility, neural network models can: (a) approximate general inference algorithms like belief propagation, combining bottom-up and top-down information, (b) solve Rosenblatt's classical superposition problem, which we link to the binding problem, and (c) do so based on an unsupervised learning approach. The framework leads to two related models: the first model shows that the use of top-down feedback significantly improves the network's ability to perform inference of corrupted inputs; the second model, including oscillatory behavior in the processing units, shows that the superposition problem can be efficiently solved based on the unit's phases.

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Yuan Liu, Guillermo A. Cecchi, A. Ravishankar Rao, James Kozloski, and Charles C. Peck "Inference and segmentation in cortical processing", Proc. SPIE 6057, Human Vision and Electronic Imaging XI, 60570Y (9 February 2006); https://doi.org/10.1117/12.650804

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