Show Reference: "Hierarchical Bayesian inference in the visual cortex."

Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America, Vol. 20, No. 7. (July 2003), pp. 1434-1448 by Tai S. S. Lee, David Mumford
@article{lee-and-mumford-2003,
    abstract = {Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.},
    address = {Computer Science Department, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. tai@cs.cmu.edu},
    author = {Lee, Tai S. S. and Mumford, David},
    issn = {1084-7529},
    journal = {Journal of the Optical Society of America},
    keywords = {algorithmic, architecture, attention, bayes, bottom-up, perception, probability, top-down, visual, visual-processing},
    month = jul,
    number = {7},
    pages = {1434--1448},
    pmid = {12868647},
    posted-at = {2013-01-03 16:54:25},
    priority = {2},
    title = {Hierarchical Bayesian inference in the visual cortex.},
    url = {http://view.ncbi.nlm.nih.gov/pubmed/12868647},
    volume = {20},
    year = {2003}
}

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Lee and Mumford interpret the visual pathway in terms of Bayesian belief propagation: each stage in the processing uses output from the one further up as contextual information and output from the one further down as evidence to update its belief and corresponding output.

Each layer thus calculates probabilities of features of the visual display given noisy and ambiguous input.

Lee and Mumford state that their dynamic, recurrent Bayesian model of the visual pathway in its simple form is prone to running into local maxima (states in which small changes in belief in any of the processing stages decrease the joint probability, although a greater changes would increase it).

They propose particle filtering as a solution which they describe as maintaining a number of concurrent high-likelihood hypotheses instead of going for the maximum likelihood one.

LGN is the earliest stage in the visual pathway receiving feedback (the retina does not).

Lee and Mumford link their theory to resonance and predictive coding.