Functional properties of models for direction selectivity in the retina

Abstract

Poggio and Reichardt (Kybernetik, 13:223–227, 1973) showed that if the average response of a visual system to a moving stimulus is directionally selective, then this sensitivity must be mediated by a nonlinear operation. In particular, it has been proposed that at the behavioral level, motion-sensitive biological systems are implemented by quadratic nonlinearities (Hassenstein and Reichardt: Z. Naturforsch., 11b:513–524, 1956; van Santen and Sperling: J. Opt. Soc. Am. [A] 1:451–473, 1984; Adelson and Bergen: J. Opt. Soc. Am. [A], 2:284–299, 1985). This paper analyzes theoretically two nonlinear neural mechanisms that possibly underlie retinal direction selectivity and explores the conditions under which they behave as a quadratic nonlinearity. The first mechanism is shunting inhibition (Torre and Poggio: Proc. R. Soc. Lond. [Biol.], 202:409–416, 1978), and the second consists of the linear combination of the outputs of a depolarizing and a hyperpolarizing synapse, followed by a threshold operation. It was found that although sometimes possible, it is in practice hard to approximate the Shunting Inhibition and the Threshold models for direction selectivity by quadratic systems. For instance, the level of the threshold on the Threshold model must be close to the steady-state level of the cell's combined synaptic input. Furthermore, for both the Shunting and the Threshold models, the approximation by a quadratic system is only possible for a small range of low contrast stimuli and for situations where the rectifications due to the ON–OFF mechanisms, and to the ganglion cells' action potentials, can be linearized. The main question that this paper leaves open is, how do we account for the apparent quadratic properties of motion perception given that the same properties seem so fragile at the single cell level? Finally, as a result of this study, some system analysis experiments were proposed that can distinguish between different instances of the models.