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.

[1]  Joel Pokorny,et al.  Responses to pulses and sinusoids in macaque ganglion cells , 1994, Vision Research.

[2]  Timothy A. Machado,et al.  Functional connectivity in the retina at the resolution of photoreceptors , 2010, Nature.

[3]  Dawei W. Dong Spatiotemporal Coupling and Scaling of Natural Images and Human Visual Sensitivities , 1996, NIPS.

[4]  J. Sanes,et al.  The most numerous ganglion cell type of the mouse retina is a selective feature detector , 2012, Proceedings of the National Academy of Sciences.

[5]  Jonathon Shlens,et al.  Uniform Signal Redundancy of Parasol and Midget Ganglion Cells in Primate Retina , 2009, The Journal of Neuroscience.

[6]  Tatyana O Sharpee,et al.  Critical and maximally informative encoding between neural populations in the retina , 2014, Proceedings of the National Academy of Sciences.

[7]  Roland J. Baddeley,et al.  Synaptic energy efficiency in retinal processing , 2003, Vision Research.

[8]  Katja Reinhard,et al.  Retinal output changes qualitatively with every change in ambient illuminance , 2014, Nature Neuroscience.

[9]  Jonathon Shlens,et al.  Spatial Properties and Functional Organization of Small Bistratified Ganglion Cells in Primate Retina , 2007, The Journal of Neuroscience.

[10]  D. Dacey,et al.  Origins of perception : retinal ganglion cell diversity and the creation of parallel visual pathways , 2011 .

[11]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[12]  A. Izenman Reduced-rank regression for the multivariate linear model , 1975 .

[13]  H. Sompolinsky,et al.  Benefits of Pathway Splitting in Sensory Coding , 2014, The Journal of Neuroscience.

[14]  Saskia E. J. de Vries,et al.  Retinal Ganglion Cells Can Rapidly Change Polarity from Off to On , 2007, PLoS biology.

[15]  Michael J. Berry,et al.  Predictive information in a sensory population , 2013, Proceedings of the National Academy of Sciences.

[16]  S. Laughlin Energy as a constraint on the coding and processing of sensory information , 2001, Current Opinion in Neurobiology.

[17]  Heinz Wässle,et al.  Parallel processing in the mammalian retina , 2004, Nature Reviews Neuroscience.

[18]  Eero P. Simoncelli,et al.  Efficient Coding of Spatial Information in the Primate Retina , 2012, The Journal of Neuroscience.

[19]  Eero P. Simoncelli,et al.  Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons , 2011, NIPS.

[20]  D. Dacey The mosaic of midget ganglion cells in the human retina , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[21]  Warren S. McCulloch,et al.  What The Frogs Eye Tells The Frogs Brain , 2015 .

[22]  J. Atick,et al.  STATISTICS OF NATURAL TIME-VARYING IMAGES , 1995 .

[23]  Joseph J. Atick,et al.  Towards a Theory of Early Visual Processing , 1990, Neural Computation.

[24]  Li Zhaoping,et al.  Understanding Vision: Theory, Models, and Data , 2014 .

[25]  D. Dacey Physiology, morphology and spatial densities of identified ganglion cell types in primate retina. , 1994, Ciba Foundation symposium.

[26]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[27]  D. Dacey,et al.  Dendritic field size and morphology of midget and parasol ganglion cells of the human retina. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[28]  THE EFFECTS OF NEURAL RESOURCE CONSTRAINTS ON EARLY VISUAL REPRESENTATIONS , 2018 .

[29]  J. H. Hateren,et al.  Theoretical predictions of spatiotemporal receptive fields of fly LMCs, and experimental validation , 1992, Journal of Comparative Physiology A.

[30]  Joseph J. Atick,et al.  What Does the Retina Know about Natural Scenes? , 1992, Neural Computation.

[31]  J. V. van Hateren,et al.  Spatiotemporal contrast sensitivity of early vision , 1993, Vision Research.

[32]  Dawei W. Dong,et al.  Spatiotemporal Inseparability of Natural Images and Visual Sensitivities , 2001 .

[33]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[34]  D. Baylor,et al.  Mosaic arrangement of ganglion cell receptive fields in rabbit retina. , 1997, Journal of neurophysiology.

[35]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[36]  Tim Gollisch,et al.  Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina , 2010, Neuron.

[37]  R. Shapley,et al.  The primate retina contains two types of ganglion cells, with high and low contrast sensitivity. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Charles P. Ratliff,et al.  Retina is structured to process an excess of darkness in natural scenes , 2010, Proceedings of the National Academy of Sciences.

[39]  Michael J. Berry,et al.  A test of metabolically efficient coding in the retina , 2002, Network.

[40]  K. Purpura,et al.  Response variability in retinal ganglion cells of primates. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[41]  JH Maunsell,et al.  Does primate motion perception depend on the magnocellular pathway? , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[42]  O. Marre,et al.  Toward a unified theory of efficient, predictive, and sparse coding , 2017, Proceedings of the National Academy of Sciences.

[43]  Pierre Yger,et al.  Multiplexed computations in retinal ganglion cells of a single type , 2016, Nature Communications.

[44]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .