Dept. of Statistics and EECS, UC Berkeley
Representation of Natural Images in V4
Monday 19th of November 2012 at 12:00pm
The functional organization of area V4 in the mammalian ventral visual pathway is far from being well understood. V4 is believed to play an important role in the recognition of shapes and objects and in visual attention, but the complexity of this cortical area makes it hard to analyze. In particular, no current model of V4 has shown good predictions for neuronal responses to natural images and there is no consensus on the primary role of V4.
In this talk, we present analysis of electrophysiological data on the response of V4 neurons to natural images. We propose a new computational model that achieves comparable prediction performance for V4 as for V1 neurons. Our model does not rely on any pre-defined image features but only on invariance and sparse coding principles. We interpret our model using sparse principal component analysis and discover two groups of neurons: those selective to texture versus those selective to contours. This supports the thesis that one primary role of V4 is to extract objects from background in the visual field. Moreover, our study also confirms the diversity of V4 neurons. Among those selective to contours, some of them are selective to orientation, others to acute curvature features. (This is joint work with J. Mairal, Y. Benjamini, B. Willmore, M. Oliver and J. Gallant.)
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