Our percepts of the world are clearly *inferred*, rather than being derived directly from the available data. This means that our brains must be endowed with powerful inferential machinery---i.e., probabilistic models---for combining incoming sensory information together with prior knowledge of the natural environment in order to infer what's "out there." This talk will describe recent efforts to characterize the statistical structure of the natural environment and its relation to neural representations in the visual system. I shall focus on a simple probabilistic model based on sparse coding in which images are described in terms of a small number of events at any given point in time. The model has been shown to be capable of predicting neural receptive field properties, and recent evidence supports the idea that sparse coding may be at work in the visual system.
Presentation (PDF File)
Video of Talk (RealPlayer File)
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