Can the theory of "whitening" explain the center-surround properties of retinal ganglion cell receptive fields?

To account for the spatial and temporal response properties of the retina, a number of studies have proposed that these properties serve to "whiten" the visual input. In particular, it has been argued that the sensitivity of retinal ganglion cells is matched to the spatial frequency spectrum of natural scenes, resulting in a flattened or "whitened" response spectrum across a range of frequencies. However, we argue that there are two distinct hypotheses regarding the flattening of the spectrum. The decorrelation hypothesis proposes that the magnitude of each ganglion cell tuning curve rises with spatial frequency, resulting in a flattened response spectrum for natural scene stimuli. With appropriate sampling, this scheme allows neighboring neurons to be uncorrelated with each other. The response equalization hypothesis proposes that the overall response magnitude of neurons increases with spatial frequency. The proposed goal of this model is to allow neurons with different receptive field sizes to produce the same average response to natural scenes. The response equalization hypothesis proposes an explanation for the relative gain of different ganglion cells and we show that this proposal fits well with published data. We suggest that both hypotheses are important in understanding the tuning and sensitivity of ganglion cells. However, using a simulation, both models are shown to be insufficient to explain the center-surround receptive field organization of ganglion cells. We discuss other factors, including representational sparseness, which could be related to the goals of ganglion cell spatial processing. We suggest three constraints needed to describe the basic linear properties of P-type ganglion cells: decorrelation, response equalization, and a minimal wiring or minimal size constraint.

Contrast adaptation in the Limulus lateral eye

Luminance and contrast adaptation are neuronal mechanisms employed by the visual system to adjust our sensitivity to light. They are mediated by an assortment of cellular and network processes distributed across the retina and visual cortex. Both have been demonstrated in the eyes of many vertebrates, but only luminance adaptation has been shown in invertebrate eyes to date. Since the computational benefits of contrast adaptation should apply to all visual systems, we investigated whether this mechanism operates in horseshoe crab eyes, one of the best-understood neural networks in the animal kingdom. The spike trains of optic nerve fibers were recorded in response to light stimuli modulated randomly in time and delivered to single ommatidia or the whole eye. We found that the retina adapts to both the mean luminance and contrast of a white-noise stimulus, that luminance- and contrast-adaptive processes are largely independent, and that they originate within an ommatidium. Network interactions are not involved. A published computer model that simulates existing knowledge of the horseshoe crab eye did not show contrast adaptation, suggesting that a heretofore unknown mechanism may underlie the phenomenon. This mechanism does not appear to reside in photoreceptors because white-noise analysis of electroretinogram recordings did not show contrast adaptation. The likely site of origin is therefore the spike discharge mechanism of optic nerve fibers. The finding of contrast adaption in a retinal network as simple as the horseshoe crab eye underscores the broader importance of this image processing strategy to vision.