Spatial asymmetries in cat retinal ganglion cell responses

Abstract

Enroth-Cugell and Robson (1966) first proposed a classification of retinal ganglion cells into X cells, which exhibit approximate linear spatial summation and largely sustained responses, and Y cells, which exhibit nonlinearities and transient responses. Gaudiano (1992a, 1992b, 1994) has suggested that the dominant characteristics of both X and Y cells can be simulated with a single model simply by changing receptive field profiles to match those of the anatomical counterparts of X and Y cells. He also proposed that a significant component of the spatial nonlinearities observed in Y (and sometimes X) cells can result from photoreceptor nonlinearities coupled with push-pull bipolar connections. Specifically, an asymmetry was predicted in the ganglion cell response to rectangular gratings presented at different locations in the receptive field under two conditions: introduction/withdrawal (on-off) or contrast reversal. When measuring the response to these patterns as a function of spatial phase, the standard difference-of-Gaussians model predicts symmetrical responses about the receptive field center, while the push-pull model predicts slight but significant asymmetry in the on-off case only. To test this hypothesis, we have recorded ganglion cell responses from the optic tract fibers of anesthetized cat. The mean and standard deviations of responses to on-off and contrast-reversed patterns were compared. We found that all but one of the cells that yielded statistically significant data confirmed the hypothesis. These results largely support the theoretical prediction.

The Influence of Different Retinal Subcircuits on the Nonlinearity of Ganglion Cell Behavior

“…For ganglion cells, this is illustrated in Figure 11. Gaudiano (1992aGaudiano ( ,b, 1994 and Gaudiano et al (1998) already suggested that receptive field size could play a role in the generation of retinal nonlinearities. Our results support this view, but the push-pull intraretinal connectivity he introduced does not seem to be necessary for nonlinear responses.…”

Neural computations in the tiger salamander and mudpuppy outer retinae and an analysis of GABA action from horizontal cells

“…Over the last four decades, several computational models have been proposed to explain the function of the retina as a whole, or of particular cell classes, in a variety of species (e.g., Rodieck 1965;Rodieck and Stone 1965a,b;Enroth-Cugell and Shapley 1973;Richter and Ullman 1982;Dawis et al 1984;Kruk and Wrobel 1986;Enroth-Cugell and Freeman 1987;Victor 1987a,b;Maguire et al 1989a;Ö g˘men and Gagne´1990b;Werblin 1991;Gaudiano 1992aGaudiano ,b,1994Maguire 1995;Shah and Levin 1996a,b;Gaudiano et al 1998;Awatramani et al 1997;Berry et al 1999;Wu et al 2000;Kamermans et al 2001). One emerging computational principle is that light adaptation is carried out by two complementary mechanisms: one is the control of transduction/transmission gain over time (temporal adaptation) (e.g., Grabowski et al 1972;Boynton and Whitten 1970;Dowling and Ripps 1972;Baylor et al 1974a,b;Carpenter and Grossberg 1981), and the second is the control of the activity distribution over the retinal network via spatial interactions among retinotopically neighboring cells (spatial adaptation) (Rodieck 1965;Rodieck and Stone 1965a,b;Richter and Ullman 1982;Fleet et al 1985;Enroth-Cugell and Freeman 1987;Ö g˘men and Gagne1 990b;Smirnakis et al 2000).…”

Brain Differently Changes its Algorithms in Parallel Processing of Visual Information

“…The push-pull mechanism in Y-cells is far away from equilibrium and therefore signal summation is strongly nonlinear with different. dynamics than X-c:ells [3,4]. In LGN the different.…”