What is the "contrast" in contrast adaptation?

Abstract

Event Abstract Back to Event What is the "contrast" in contrast adaptation? During natural vision, retinal ganglion cells encode enormous variations of light intensity. In response, they adapt to the luminance distribution?s mean and width, the latter usually quantified by contrast (C = standard deviation / mean). This contrast adaptation compresses the large dynamic range of stimuli into a narrow range of firing rates. Here we describe ganglion cell responses via a linear-nonlinear model and ask, how should the nonlinearity adapt to different stimulus ensembles to efficiently fill the cells "response bandwidth" To study this, we passed each stimulus through a fixed linear filter followed by rectification and a nonlinearity, the output of which is taken to model an OFF ganglion cell?s firing rate. We then sought the nonlinearity that maximized information transfer subject to a constraint on the peak firing rate. The predicted nonlinearity maps the distribution of filter outputs to a uniform distribution of firing rates. An added mean rate constraint gives a truncated exponential rate distribution. First, we considered a Gaussian stimulus ensemble. For this ensemble, the model predicts a sigmoidal nonlinearity with a gain (slope at inflection point) proportional to 1/C. To test this, we made patch-clamp recordings of guinea pig retinal ganglion cells subject to full-field white noise stimulation at several contrasts. We found very close agreement between the predicted and measured nonlinearities at each contrast level. Natural light fluctuations are strongly non-Gaussian: they have temporal correlations and a skewed distribution. To study how this affects the optimal nonlinearity, we sorted a natural time series (van Hateren, 1997) into segments of fixed contrast. For all segment lengths we found that the distribution of contrasts was wide and skewed (peak C ~ 0.5 and 10% of segments with C > 1.5). In addition, for any given contrast, the light intensity distribution was also highly skewed. As above, we filtered and rectified segments of a given contrast; the resulting optimal gain was proportional to 1/C^0.5. This behavior differs from the Gaussian results in that it predicts less adaptation to increasing contrast. This is because skewed stimuli feature large but rare intensity fluctuations that strongly affect C, but excessive adaptation to these outliers would render the cell insensitive to the much more frequent small intensities. These considerations led us to ask whether a measure of contrast which is robust to outliers would be more natural for skewed intensity distributions. One possibility is the q-width, namely the range around the mean containing q% of intensity values. For Gaussian stimuli the q-width is proportional to the standard deviation. We found that the optimal gain predicted from the natural time series went approximately as the reciprocal of the 60-width. Thus, using this measure, the predicted gains have the same functional form for both naturally skewed and Gaussian distributions. Experiments in preparation will measure contrast adaptation in response to naturalistic stimuli, allowing us to test the prediction that contrast gain control is insensitive to outliers. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). What is the "contrast" in contrast adaptation?. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.144 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.