Christoph Zetzsche
Universit├Ąt Bremen

Nonlinear Neural Selectivity and the Statistics of Natural Scenes

Monday 11th of August 2014 at 02:00pm
560 Evans - ** note different time **

The classic linear filter model has been tremendously successful in advancing our understanding of visual perception and of the neural machinery of primary visual cortex. Its key feature is the selectivity to spatial frequencies as obtained by wavelet-like bandpass filter mechanisms with different sizes and orientations. All visual processing beyond this linear stage remained somewhat enigmatic, however, and despite a wealth of empirical information about various nonlinear phenomena in visual perception and cortical processing we have never again achieved a consensus like that for the standard model about a suitable approach to the interpretation and modeling of these nonlinear phenomena. In this talk I will suggest two basic nonlinear properties that may be of help for structuring our understanding of nonlinear vision. Both address the key issue of selectivity: In how far are the nonlinear operations different from the selectivity obtained with linear filters? The first aspect refers to the "tuning width" of selectivity as determined by the shape of the nonlinear response surface in state space. This response surface is curved, thereby increasing selectivity beyond that obtainable with the planar response surfaces of linear filters. We will show that this property can explain phenomena like the overcompleteness of the representation in primary visual cortex, as well as certain strange extraclassical receptive field effects, for example the context-dependent switch from a suppressive to a facilitatory influence of a given stimulus component. The second property is a new concept for the "tuning direction": Beyond the selectivity for spatial frequencies that is provided by linear filter mechanisms, the nonlinear operations can provide a selectivity for AND-like combinations of frequencies. If the frequencies differ in orientation this amounts to a basic dimension of visual encoding: the nonlinear selectivity for intrinsically two-dimensional signals. We show that this type of selectivity can provide a unifying explanation for a large class of recent observations on the selectivity of neurons in visual cortex.

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