HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 98/3/1287    most recent 00618.2006v1 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 ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Gaudry, K. S. Articles by Reinagel, P. Search for Related Content PubMed PubMed Citation Articles by Gaudry, K. S. Articles by Reinagel, P.

Contrast Adaptation in a Nonadapting LGN Model

Department of Neurobiology, University of California, San Diego, La Jolla, California

Submitted 13 June 2006; accepted in final form 26 June 2007

Sensory neurons appear to adapt their gain to match the variance of signals along the dimension they encode, a property we shall call "contrast normalization." Contrast normalization has been the subject of extensive physiological and theoretical study. We previously found that neurons in the lateral geniculate nucleus (LGN) exhibit contrast normalization in their responses to full-field flickering white-noise stimuli, and that neurons with the strongest contrast normalization best preserved information transmission across a range of contrasts. We have also shown that both of these properties could be reproduced by nonadapting model cells. Here we present a detailed comparison of this nonadapting model to physiological data from the LGN. First, the model cells recapitulated other contrast dependencies of LGN responses: decreasing stimulus contrast resulted in an increase in spike-timing jitter and spike-number variability. Second, we find that the extent of contrast normalization in this model depends on model parameters related to refractoriness and to noise. Third, we show that the model cells exhibit rapid, transient changes in firing rate just after changes in contrast, and that this is sufficient to produce the transient changes in information transmission that have been reported in other neurons. It is known that intrinsic properties of neurons change during contrast adaptation. Nevertheless the model demonstrates that the spiking nonlinearity of neurons can produce many of the temporal aspects of contrast gain control, including normalization to input variance and transient effects of contrast change.