BibTeX
@MISC{Yuille_probabilistictheories,
author = {A. L. Yuille and D. Kersten},
title = {Probabilistic Theories of the Visual Cortex},
year = {}
}
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Abstract
THE VERY EARLY VISUAL SYSTEM This lecture first briefly reviews the structural organization of V1, the properties of simple cells, and divisive normalization. The lecture also illustrated principles such as sparsity, independence, and inverting generative models. A. Review: From Retina and LGN to V1 Light is captured in the retina, transmitted to the LGN, and then to area V1 of the visual cortex. Receptive field properties of neurons in retina and LGN are generally believed to be modelled by symmetric centersurround cells – i.e. the Laplacian of a Gaussian filter, which looks like a Mexican Hat. This may be an over-simplification (e.g., see meister for an alternative viewpoint) but Yang Dan reports that it is possible to reconstruct the input image from the responses of neurons in retina or LGB (which would seem to be impossible if the standard models were badly wrong). There is an expansion (by a factor between 80 and 400) as we move from the LGN to V1. This is not surprising because V1 starts the hard problem of interpreting the image (while the retina and LGN perform the simpler tasks of capturing the image and transmitting it to the cortex – at least this is the
Keyphrases
visual cortex probabilistic theory receptive field property hard problem yang dan area v1 input image simple cell gaussian filter symmetric centersurround cell early visual system v1 light structural organization simpler task divisive normalization generative model mexican hat standard model alternative viewpoint
