A Biophysically Inspired Model for Contrast Adaptation

Abstract

Event Abstract Back to Event A Biophysically Inspired Model for Contrast Adaptation Contrast adaptation is a process that changes the gain, kinetics and baseline membrane potential of retinal ganglion cells over timescales that range from less than 100 ms to tens of seconds. Several models have been proposed that can replicate some of these adaptive processes (Borst et al, 2005, Gaudry, Reinagel 2007, Mante et al, 2008), although it is not clear how these models translate into biophysical mechanisms. Here we present a simple biophysically inspired model of an adaptive cell that tracks the gain with sub-second precision and replicates fast kinetic and baseline membrane potential changes of salamander retinal ganglion cells over a wide range of contrasts. The model is comprised of three sequential stages, a linear filter, a static nonlinearity and a three state first order kinetic model. We recorded from ganglion cells intracellularly while presenting a uniform field visual stimulus. The light intensity was drawn from a Gaussian distribution every 30 ms, and every 20 s the standard deviation of the distribution changed randomly to between 10 % and 40 % of the mean intensity. The parameters of the model were fit using a constrained optimization method to minimize the error between the model and the membrane potential response. The overall correlation between the actual and estimated responses was 72% for a single set of model parameters whereas it was 65% when we used many separate Linear-Nonlinear (LN) models fit to each contrast. In the adaptive stage of the model, the states can be thought of as representing “available”, “active” and “unavailable” states of a biophysical mechanism. This flexible framework can be applied to ion channels that inactivate, synapse vesicle pools that experience depression, or neurotransmitter receptors that desensitize. With three states and one rate constant that is set by the output of the static nonlinearity, increases in contrast produce a Weber-like change in gain, an acceleration in kinetics, an increase in baseline, and asymmetric dynamics in the response, as is seen experimentally. These changes are intrinsic to the adaptive stage without the need for an additional pathway to control the response properties. The incorporation of an additional state or input-dependent rate constant produces additional, slower timescales of adaptation. Although this model is applied here to the process of contrast adaptation, the adaptive stage changes the overall response properties by virtue of a change in mean produced by the output of the threshold nonlinearity at different contrasts. As such, this type of adaptive stage may be applicable to other types of adaptation such as luminance adaptation with a different input stage. A simple biophysical model can capture multiple phenomena of adaptation, indicating that seemingly distinct adaptive processes could be generated even by a single molecule with just a few states. The parameters of this model can be to used design and interpret experiments on the mechanisms that produce these adaptive properties. 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). A Biophysically Inspired Model for Contrast Adaptation. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.346 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: 10 Feb 2009; Published Online: 10 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.