Modeling Retina Adaptation with Multiobjective Parameter Fitting

Abstract

The retina continually adapts its kinetics, average response and sensitivity to the conditions of the environment. Retinal neurons adapt essentially to the mean light intensity and its temporal fluctuations over the mean, also called temporal contrast. Contrast adaptation has two distinct temporal expressions with fast and slow components. Here, we present a configurable retina simulation environment that accurately reproduces both contrast components. A contrast increase in the visual input accelerates kinetics of the filter, reduces sensitivity and depolarizes the membrane potential. Slow adaptation does not affect the temporal response but produces a progressive hyperpolarization of membrane potential. The implemented model for contrast adaptation provides a neural basis of each retinal stage, from photoreceptors up to ganglion cells, to explain the observed retina behavior. Both forms of contrast adaptation, fast and slow, are captured by a combined model of shunting feedback of bipolar cells and short-term plasticity (STP) at the bipolar-to-ganglion synapse. Biological accuracy of the model is evaluated by comparison of the measured neural response with the simulated response fitted to published physiological data. One problem with the simulated model is finding its optimal parameter settings, since the model response is described by a complex system of different retina stages with linear, nonlinear and feedback connections. We propose to use a multiobjective genetic optimization to automatically search the parameter space and easily find a feasible configuration solution.