Retinal Processing Optimizes Contrast Coding
|Title||Retinal Processing Optimizes Contrast Coding|
|Year of Publication||2016|
|Authors||Kim J, Batard T, Bertalmío M|
|Image Source Program||Vision Sciences Society Annual Meeting|
The properties of human contrast perception show a close correlation to the responses of retinal ganglion cells (Kelly, 1977; Lee, 1990), whose spatial processing properties (the isotropic center-surround processing) are shaped by the local feedback from interneurons (horizontal / amacrine cells) to the feed-forward cells (photoreceptor, and bipolar / ganglion cells). In the current work we investigated the computational structure of this retinal feedback system. We first identified a simple form of a system of differential equations that realizes the retinal feedback architecture and analyzed its steady-state behaviour to a static stimulus input. Three main conclusions may be derived from the results of the analysis. Firstly, the system of equations preserves the ability to predict some human contrast perception properties such as spatial-frequency dependent contrast sensitivity and brightness induction (contrast and assimilation) as other existing retinal models predict (Kim & Bertalmío, 2015; submitted; van Hateren, 2007; Wilson, 1997), thus showing a minimum computational structure to emulate human contrast perception engendered at the retina. Secondly, the steady-state response of the system can be obtained in a single pass by convolving the original input with a single kernel (a combined product of different extents of receptive-fields of the retinal cells) and therefore our work proposes a computationally efficient way of modeling retinal cell responses and the resulting human contrast perception. Finally, finding the steady state solution is mathematically equivalent to solving an optimization problem of maximizing the spatial contrast in the encoded signals while being faithful to the local light intensity of the input stimulus, which suggests interesting connections with efficient coding theories and computational neuroscience models like the Wilson-Cowan equations (see Bertalmío, 2014). Our results shed light on the computational goal of the feedback architecture in the retinal circuit: an optimized representation of the spatial contrast in the incoming light pattern.