A Computational Framework for Realistic Retina Modeling

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

[1]  David Tsai,et al.  Understanding the retina: a review of computational models of the retina from the single cell to the network level. , 2014, Critical reviews in biomedical engineering.

[2]  Tim Gollisch,et al.  Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction , 2006, Science.

[3]  R. Shapley,et al.  Light adaptation in the primate retina: Analysis of changes in gain and dynamics of monkey retinal ganglion cells , 1990, Visual Neuroscience.

[4]  B. Borghuis,et al.  Cellular Basis for Contrast Gain Control over the Receptive Field Center of Mammalian Retinal Ganglion Cells , 2007, The Journal of Neuroscience.

[5]  Oscar Estévez Uscanga,et al.  On the fundamental data-base of normal and dichromatic color vision , 1979 .

[6]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[7]  Pablo Martínez-Cañada,et al.  Towards a Generic Simulation Tool of Retina Models , 2015, IWINAC.

[8]  K. D. De Valois,et al.  A multi-stage color model. , 1993, Vision research.

[9]  Michael J. Berry,et al.  Anticipation of moving stimuli by the retina , 1999, Nature.

[10]  S. Morad,et al.  Ceramide-orchestrated signalling in cancer cells , 2012, Nature Reviews Cancer.

[11]  Sovira Tan,et al.  Performance of three recursive algorithms for fast space-variant Gaussian filtering , 2003, Real-time imaging.

[12]  J. B. Demb,et al.  Contrast Adaptation in Subthreshold and Spiking Responses of Mammalian Y-Type Retinal Ganglion Cells , 2005, The Journal of Neuroscience.

[13]  Kerry J. Kim,et al.  Slow Na+ Inactivation and Variance Adaptation in Salamander Retinal Ganglion Cells , 2003, The Journal of Neuroscience.

[14]  F. Amthor,et al.  Nonlinearity of the inhibition underlying retinal directional selectivity , 1991, Visual Neuroscience.

[15]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[16]  David Tschumperlé,et al.  The CImg Library , 2012 .

[17]  T. Poggio,et al.  A synaptic mechanism possibly underlying directional selectivity to motion , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[18]  C. M. Davenport,et al.  Parallel ON and OFF Cone Bipolar Inputs Establish Spatially Coextensive Receptive Field Structure of Blue-Yellow Ganglion Cells in Primate Retina , 2009, The Journal of Neuroscience.

[19]  M. Carandini,et al.  A Synaptic Explanation of Suppression in Visual Cortex , 2002, The Journal of Neuroscience.

[20]  D. Tranchina,et al.  Retinal light adaptation—evidence for a feedback mechanism , 1984, Nature.

[21]  R. Deriche Recursively implementating the Gaussian and its derivatives , 1993 .

[22]  Hans van Hateren A cellular and molecular model of response kinetics and adaptation in primate cones and horizontal cells. , 2005, Journal of vision.

[23]  J. B. Demb,et al.  Bipolar Cells Contribute to Nonlinear Spatial Summation in the Brisk-Transient (Y) Ganglion Cell in Mammalian Retina , 2001, The Journal of Neuroscience.

[24]  J. B. Demb,et al.  Presynaptic Mechanism for Slow Contrast Adaptation in Mammalian Retinal Ganglion Cells , 2006, Neuron.

[25]  Pablo Martínez-Cañada,et al.  First Stage of a Human Visual System Simulator: The Retina , 2015, CCIW.

[26]  Kerry J. Kim,et al.  Temporal Contrast Adaptation in the Input and Output Signals of Salamander Retinal Ganglion Cells , 2001, The Journal of Neuroscience.

[27]  Jeanny Hérault A model of colour processing in the retina of vertebrates: From photoreceptors to colour opposition and colour constancy phenomena , 1996, Neurocomputing.

[28]  Alice Caplier,et al.  Using Human Visual System modeling for bio-inspired low level image processing , 2010, Comput. Vis. Image Underst..

[29]  Stephen A. Baccus,et al.  Segregation of object and background motion in the retina , 2003, Nature.

[30]  Multiplying two numbers together in your head is a difficult task if you did not learn multiplication tables as a child. On the face of it, this is somewhat surprising given the remarkable power of the brain to perform , 2010 .

[31]  E. Chichilnisky,et al.  Adaptation to Temporal Contrast in Primate and Salamander Retina , 2001, The Journal of Neuroscience.

[32]  R A Young,et al.  The Gaussian derivative model for spatial vision: I. Retinal mechanisms. , 1988, Spatial vision.

[33]  Erik De Schutter,et al.  Automated neuron model optimization techniques: a review , 2008, Biological Cybernetics.

[34]  Bill Triggs,et al.  Boundary conditions for Young-van Vliet recursive filtering , 2006, IEEE Transactions on Signal Processing.

[35]  Jonathan B Demb,et al.  Distinct expressions of contrast gain control in parallel synaptic pathways converging on a retinal ganglion cell , 2008, The Journal of physiology.

[36]  D. Dacey,et al.  Receptive field structure of H1 horizontal cells in macaque monkey retina. , 2002, Journal of vision.

[37]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[38]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[39]  M. Carandini,et al.  Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.

[40]  James M. Bower,et al.  The Book of GENESIS , 1994, Springer New York.

[41]  C. Enroth-Cugell,et al.  The contrast sensitivity of retinal ganglion cells of the cat , 1966, The Journal of physiology.

[42]  Pierre Kornprobst,et al.  Virtual Retina: A biological retina model and simulator, with contrast gain control , 2009, Journal of Computational Neuroscience.

[43]  Barry B. Lee,et al.  Spatial distributions of cone inputs to cells of the parvocellular pathway investigated with cone-isolating gratings. , 2012, Journal of the Optical Society of America. A, Optics, image science, and vision.

[44]  Barry B. Lee,et al.  Processing of Natural Temporal Stimuli by Macaque Retinal Ganglion Cells , 2002, The Journal of Neuroscience.

[45]  Antonio Martínez-Álvarez,et al.  A design framework to model retinas , 2007, Biosyst..

[46]  J. B. Demb,et al.  Different Circuits for ON and OFF Retinal Ganglion Cells Cause Different Contrast Sensitivities , 2003, The Journal of Neuroscience.

[47]  F. Rieke,et al.  Light adaptation in cone vision involves switching between receptor and post-receptor sites , 2007, Nature.

[48]  Rava Azeredo da Silveira,et al.  Dynamical Adaptation in Photoreceptors , 2013, PLoS Comput. Biol..

[49]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[50]  J. Ruppersberg Ion Channels in Excitable Membranes , 1996 .

[51]  Stephen A. Baccus,et al.  A Retinal Circuit That Computes Object Motion , 2008, The Journal of Neuroscience.

[52]  Rachid Deriche Fast Algorithms for Low-Level Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[53]  Kwabena Boahen,et al.  Optic nerve signals in a neuromorphic chip I: Outer and inner retina models , 2004, IEEE Transactions on Biomedical Engineering.

[54]  S. Baccus,et al.  Coordinated dynamic encoding in the retina using opposing forms of plasticity , 2011, Nature Neuroscience.

[55]  J. Victor The dynamics of the cat retinal X cell centre. , 1987, The Journal of physiology.

[56]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[57]  Marc-Oliver Gewaltig,et al.  NEST (NEural Simulation Tool) , 2007, Scholarpedia.

[58]  Stephen A. Baccus,et al.  Retinal Adaptation to Object Motion , 2007, Neuron.

[59]  Frances S. Chance,et al.  Synaptic Depression and the Temporal Response Characteristics of V1 Cells , 1998, The Journal of Neuroscience.

[60]  Pablo Martínez-Cañada,et al.  Modeling Retina Adaptation with Multiobjective Parameter Fitting , 2015, IWANN.

[61]  Zhaoping Li Different Retinal Ganglion Cells have Different Functional Goals , 1992, Int. J. Neural Syst..

[62]  J. Pokorny,et al.  Primate horizontal cell dynamics: an analysis of sensitivity regulation in the outer retina. , 2001, Journal of neurophysiology.

[63]  J. Movshon,et al.  Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex , 1997, The Journal of Neuroscience.

[64]  Joel Pokorny,et al.  Sequential processing in vision: The interaction of sensitivity regulation and temporal dynamics , 2008, Vision Research.

[65]  S. Baccus,et al.  Linking the Computational Structure of Variance Adaptation to Biophysical Mechanisms , 2012, Neuron.

[66]  Paul R. Martin,et al.  Retinal connectivity and primate vision , 2010, Progress in Retinal and Eye Research.

[67]  F. Rieke Temporal Contrast Adaptation in Salamander Bipolar Cells , 2001, The Journal of Neuroscience.

[68]  S. Baccus,et al.  Building Blocks of Temporal Filters in Retinal Synapses , 2014, PLoS biology.

[69]  M. Carandini,et al.  Functional Mechanisms Shaping Lateral Geniculate Responses to Artificial and Natural Stimuli , 2008, Neuron.

[70]  M. Meister,et al.  Fast and Slow Contrast Adaptation in Retinal Circuitry , 2002, Neuron.

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

[72]  Nicole C. Rust,et al.  Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.

[73]  J. B. Demb,et al.  Functional circuitry of visual adaptation in the retina , 2008, The Journal of physiology.

[74]  Jonathan B Demb,et al.  Multiple Mechanisms for Contrast Adaptation in the Retina , 2002, Neuron.

[75]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[76]  Christof Koch,et al.  An Analog VLSI Inplementation of a Visual Interneuron Enhanced Sensory Processing Through Biophysical Modeling , 1999, Int. J. Neural Syst..

[77]  Romain Brette,et al.  The Brian Simulator , 2009, Front. Neurosci..

[78]  Jeanny Hérault,et al.  Modeling Visual Perception for Image Processing , 2007, IWANN.

[79]  Barry B. Lee,et al.  Dynamics of sensitivity regulation in primate outer retina: the horizontal cell network. , 2003, Journal of vision.

[80]  Henry Markram,et al.  A Novel Multiple Objective Optimization Framework for Constraining Conductance-Based Neuron Models by Experimental Data , 2007, Front. Neurosci..