The emergence of multiple retinal cell types through efficient coding of natural movies
One of the most striking aspects of early visual processing in the retina is the immediate parcellation of visual information into multiple parallel pathways, formed by different retinal ganglion cell types each tiling the entire visual field. Existing theories of efficient coding have been unable to account for the functional advantages of such cell-type diversity in encoding natural scenes. Here we go beyond previous theories to analyze how a simple linear retinal encoding model with different convolutional cell types efficiently encodes naturalistic spatiotemporal movies given a fixed firing rate budget. We find that optimizing the receptive fields and cell densities of two cell types makes them match the properties of the two main cell types in the primate retina, midget and parasol cells, in terms of spatial and temporal sensitivity, cell spacing, and their relative ratio. Moreover, our theory gives a precise account of how the ratio of midget to parasol cells decreases with retinal eccentricity. Also, we train a nonlinear encoding model with a rectifying nonlinearity to efficiently encode naturalistic movies, and again find emergent receptive fields resembling those of midget and parasol cells that are now further subdivided into ON and OFF types. Thus our work provides a theoretical justification, based on the efficient coding of natural movies, for the existence of the four most dominant cell types in the primate retina that together comprise 70% of all ganglion cells.
The emergence of multiple retinal cell types through efficient coding of natural movies  This papers proposes to study possible distributions of retinal cell types. It builds upon existing literature on four cell types and evaluate how two different configurations of cells could optimally encode natural movies with a unique cell firing rate budget. It draws analogies with optimal configurations and configuration existing in primates. Models are mention to capture 90% of all ganglion cells.  The paper tackle an interesting question on wether retinal cell configurations are mathematically optimized for spatio-temporal stimuli. As a disclaimer, I am not an expert on retinal cells. The paper refers to important literature on the matter and focus on mathematical models of cell distributions. However, little information is provided on the type of target visual stimuli. Naively, one may think that different stimuli should be captured differently across varying cell distributions, and one may question why choosing to study one configuration of cells that optimized with one particular stimuli.   On the model itself, the firing-rate budget is clear. However, the link with neural networks is less clear. Is the neural net used here historically to truly mimic a biological neural network? Could other models be used, is there a particular architecture, number of layers, to follow in such case?   The paper evaluates a the performance of a mixture of 2 types of low/high spatial/temporal cells, and compares it with a configuration of one unique cell types. Why two types and not more configurations?  Minor, what is motivation of scaling (eq 3) - notation of footnote (2) can be confusing (squared?) - footnote (4): empirically? - ref [6]? - there are 8 appendices, pushing the length up for this submission  The authors' feedback proposed clarifications on major points, including on distributions of stimuli (statistics) vs specific stimuli - overall indicating importance of this work on receptive field configurations.