A unified neural network model of spatiotemporal processing in X and Y retinal ganglion cells

Abstract

This article makes use of a push-pull shunting network, which was introduced in the companion article, to model certain properties of X and Y retinal ganglion cells. Input to the push-pull network is preprocessed by a nonlinear mechanism for temporal adaptation, which is ascribed here to photoreceptor dynamics. The complete circuit is used to show that a simple change in receptive field morphology within a single model equation can change the network's response characteristics to closely resemble those of either X or Y cells. Specifically, an increase in width of the receptive field center mechanism is sufficient to account for generation of on-off (Y-like) instead of null (X-like) responses to modulated gratings. In agreement with experimental data, the Y cell on-off response is independent of spatial phase. Also, the model accurately predicts that on-off responses can be observed in X cells for particular stimulus configurations. Taken together, the results show how the retina combines individually inadequate modules to efficiently handle the tasks required for accurate spatial and temporal visual information processing. The model is also able to clarify a number of controversial experimental findings on the nature of spatiotemporal visual processing in the retina.

Spatial asymmetries in cat retinal ganglion cell responses

“…A few years ago, one of us (Gaudiano 1992a(Gaudiano , 1992b) proposed a push-pull model of X and Y ganglion cells, which was later used to provide detailed ®ts of the original results of Enroth-Cugell and Robson (Gaudiano 1994). According to the push-pull model, incoming light is ®rst transformed into neural signals by photoreceptors through a dynamic gain control mechanism.…”

“…To see this, we consider the simplest form of the photoreceptor equation utilized in the pushpull model (Gaudiano 1992b(Gaudiano , 1994. It is assumed that the response of the photoreceptor is proportional to the input, multiplied by a gain term that¯uctuates in response to input variations.…”

“…The details of the pushpull model, which were originally published in this journal (Gaudiano 1992a(Gaudiano , 1992b, have been the subject of some debate (e.g., Freed and Nelson 1994). The goal of this paper is to show that the model was able to generate a testable prediction that is supported by the data.…”

“…Gaudiano (1992aGaudiano ( , 1992bGaudiano ( , 1994 has suggested that the dominant characteristics of both X and Y cells can be simulated with a single model simply by changing receptive ®eld pro®les to match those of the anatomical counterparts of X and Y cells. He also proposed that a signi®cant component of the spatial nonlinearities observed in Y (and sometimes X) cells can result from photoreceptor nonlinearities coupled with push-pull bipolar connections.…”

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.…”