Neural models and physiological reality

Neural models of retinal processing provide an important tool for analyzing retinal signals and their functional significance. However, it is here argued that in biological reality, retinal connectivity is unlikely to be as specific as ideal neural models might suggest. The retina is thought to provide functionally specific signals, but this specificity is unlikely to be anatomically complete. This is illustrated by examples of cone connectivity to macaque ganglion cells. For example, cells of the magnocellular pathway appear to avoid short-wavelength cone input, so that such input is negligible under normal conditions. However, there is anatomical, physiological, and psychophysical evidence that under special conditions, weak input may be revealed. Second, ideal models of how retinal information is centrally utilized have to take into account the biological reality of retinal signals. The stochastic nature of impulse trains modifies signal-to-noise ratio in unexpected ways. Also, non-linearities in cell responses make, for example, multiplexing of luminance and chromatic signals in the parvocellular pathway impracticable. The purpose of this analysis is to show than ideal neural models must confront an often more complex and nuanced physiological reality.