Optimal filters under biological constraints predict population coding of retinal ganglion cells.

Eizaburo Doi, Doru C. Balcan, & Michael S. Lewicki

Society for Neuroscience Abstract, 2006.

Abstract
The visual system must encode a visual stimulus reliably in spite of several sources of noise and biological constraints. First, the neural code has limited precision (only 1~3 bits/spike). Second, photoreceptors do not output a clean image signal, but one that has been distorted by optical blur and noise in photon transduction. Finally, the biological limitations on synaptic strength and dendritic field size also constrain the neural code. Here we address the problem of how a noisy neural population represents the underlying, undistorted image signal reliably subject to the limited neural resources. We derived theoretically optimal filters (receptive fields) by training on a large set of natural images to which the visual system is assumed to be adapted. This yielded a wide range of theoretical predictions for the retinal ganglion cells. The optimal filters show center-surround organization, whose characteristic changes from band-pass to low-pass as sensory noise increases. Accordingly, the receptive fields become larger and more overlapped with each other (Fig. 1). Our model can be applied to any retinal eccentricity and predicts different characteristics. Fig. 1 shows the results for the central fovea, while Fig. 2 for the 40 degrees retinal eccentricity as in some physiological experiments (Frechette et al., 2005). The proposed model is more comprehensive than preceding ones (Srinivasan, Laughlin and Dubs, 1982; Atick, 1992; Vincent and Baddeley, 2003) because it more accurately incorporates sources of noise and biological constraints, and predicts the theoretically optimal retinal population codes for these conditions.



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