Pietro Berkes
Volen Center for Complex Systems, Brandeis University

Generative models of vision: from sparse coding toward structured models

Wednesday 16th of December 2009 at 12:00pm
508-20 Evans Hall

From a computational perspective, one can think of visual perception as the problem of analyzing the light patterns detected by the retina to recover their external causes. This process requires combining the incoming sensory evidence with internal prior knowledge about general properties of visual elements and the way they interact, and can be formalized in a class of models known as causal generative models. In the first part of the talk, I will discuss the first and most established generative model, namely the sparse coding model. Sparse coding has been largely successful in showing how the main characteristics of simple cells receptive fields can be accounted for based uniquely on the statistics of natural images. I will briefly review the evidence supporting this model, and contrast it with recent data from the primary visual cortex of ferrets and rats showing that the sparseness of neural activity over development and anesthesia seems to follow trends opposite to those predicted by sparse coding. In the second part, I will argue that the generative point of view calls for models of natural images that take into account more of the structure of the visual environment. I will present a model that takes a first step in this direction by incorporating the fundamental distinction between identity and attributes of visual elements. After learning, the model mirrors several aspects of the organization of V1, and results in a novel interpretation of complex and simple cells as parallel population of cells, coding for different aspects of the visual input. Further steps toward more structured generative models might thus lead to the development of a more comprehensive account of visual processing in the visual cortex.

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