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This Article Full Text Full Text (PDF) All Versions of this Article: 99/5/2496    most recent 01397.2007v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Sharpee, T. O. Articles by Stryker, M. P. PubMed PubMed Citation Articles by Sharpee, T. O. Articles by Stryker, M. P.

On the Importance of Static Nonlinearity in Estimating Spatiotemporal Neural Filters With Natural Stimuli

¹Sloan-Swartz Center for Theoretical Neurobiology and Department of Physiology, University of California, San Francisco; ²Crick-Jacobs Center for Theoretical and Computational Biology, Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California; and ³Center for Theoretical Neuroscience and Department of Neuroscience, College of Physicians and Surgeons, Columbia University, New York, New York

Submitted 26 December 2007; accepted in final form 17 March 2008

Understanding neural responses with natural stimuli has increasingly become an essential part of characterizing neural coding. Neural responses are commonly characterized by a linear–nonlinear (LN) model, in which the output of a linear filter applied to the stimulus is transformed by a static nonlinearity to determine neural response. To estimate the linear filter in the LN model, studies of responses to natural stimuli commonly use methods that are unbiased only for a linear model (in which there is no static nonlinearity): spike-triggered averages with correction for stimulus power spectrum, with or without regularization. Although these methods work well for artificial stimuli, such as Gaussian white noise, we show here that they estimate neural filters of LN models from responses to natural stimuli much more poorly. We studied simple cells in cat primary visual cortex. We demonstrate that the filters computed by directly taking the nonlinearity into account have better predictive power and depend less on the stimulus than those computed under the linear model. With noise stimuli, filters computed using the linear and LN models were similar, as predicted theoretically. With natural stimuli, filters of the two models can differ profoundly. Noise and natural stimulus filters differed significantly in spatial properties, but these differences were exaggerated when filters were computed using the linear rather than the LN model. Although regularization of filters computed under the linear model improved their predictive power, it also led to systematic distortions of their spatial frequency profiles, especially at low spatial and temporal frequencies.