Hierarchical Bayesian Inference in the Visual Cortex

Hierarchical Bayesian Inference in the Visual Cortex (2002)

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by Tai Sing Lee , David Mumford

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Datum	Value	Source
TITLE	Hierarchical Bayesian Inference in the Visual Cortex	SVM HeaderParse 0.1
AUTHOR NAME	Tai Sing Lee	user correction - Legacy Corrections
AUTHOR AFFIL	Rm 115, Mellon Institute; Computer Science Department; Center for the Neural Basis of Cognition; Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	Pittsburgh, PA 15213	user correction - Legacy Corrections
AUTHOR NAME	David Mumford	user correction - Legacy Corrections
AUTHOR AFFIL	Corresponding author: (for editor's information); Dr. Tai Sing Lee, Rm 115, Mellon Institute, Ctr for the Neural Basis of Cognition, Computer; Science Department, Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR ADDR	4400 Fifth Avenue, Pittsburgh, PA 15213.	user correction - Legacy Corrections
ABSTRACT	this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as top-down feedback disambiguating low level representations? Rao and Ballard's predictive coding/Kalman filter model 6 did integrate generafive feedback in the perceptual inference process, but it was primarily a linear model and thus severely limited in practical utility. The data-driven Markov Chain Monte Carlo approach of Zhu and colleagues 7, 8 might be the most successful recent application of this proposal in solving real and difficult computer vision problems using generafive models, though its connection to the visual cortex has not been explored. Here, we bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical process- ing and the nature of representations in the visual cortex. We will review some of our and others' neurophysiological experimental data to lend support to these ideas	user correction - Legacy Corrections
YEAR	2002	user correction - Legacy Corrections
CITATIONS	74 found	ParsCit 1.0