1 Basic Concepts and Overview

In this chapter, we present a modern perspective and conceptualization of early vision in terms of computational models described using the mathematics of filtering, probabilities and graphical models. This mathematical framework has the advantage that it is increasingly being used in computer vision, in the modeling of neurons and neural circuits, in models of human visual behavior, and in the analysis of neural data by statistical and machine learning techniques. Hence it enables us to describe vision from multiple perspectives from a unified framework. ? DRAFT June 2015. To appear as a chapter in From Neuron to Cognition via Computational Neuroscience, M.A. Arbib, James J. Bonaiuto Editors, Cambridge MA: The MIT Press, in 2016.

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

[2]  Sanja Fidler,et al.  The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  George Papandreou,et al.  Modeling Image Patches with a Generic Dictionary of Mini-epitomes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Bartlett W. Mel,et al.  Computing local edge probability in natural scenes from a population of oriented simple cells. , 2013, Journal of vision.

[5]  D. Kersten,et al.  Segmentation decreases the magnitude of the tilt illusion. , 2013, Journal of vision.

[6]  Christopher W Tyler,et al.  Recurrent Connectivity Can Account for the Dynamics of Disparity Processing in V1 , 2013, The Journal of Neuroscience.

[7]  M. Carandini,et al.  Normalization as a canonical neural computation , 2013, Nature Reviews Neuroscience.

[8]  Alessandra Angelucci,et al.  Different Orientation Tuning of Near- and Far-Surround Suppression in Macaque Primary Visual Cortex Mirrors Their Tuning in Human Perception , 2013, The Journal of Neuroscience.

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

[10]  Bevil R. Conway,et al.  Toward a Unified Theory of Visual Area V4 , 2012, Neuron.

[11]  Jason M Samonds,et al.  Relative luminance and binocular disparity preferences are correlated in macaque primary visual cortex, matching natural scene statistics , 2012, Proceedings of the National Academy of Sciences.

[12]  B. Tjan,et al.  The Perception of a Face Is No More Than the Sum of Its Parts , 2012, Psychological science.

[13]  Curtis L Baker,et al.  Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness , 2012, The Journal of Neuroscience.

[14]  A. Borst,et al.  Seeing Things in Motion: Models, Circuits, and Mechanisms , 2011, Neuron.

[15]  Long Zhu,et al.  Recursive Compositional Models for Vision: Description and Review of Recent Work , 2011, Journal of Mathematical Imaging and Vision.

[16]  Pushmeet Kohli,et al.  Markov Random Fields for Vision and Image Processing , 2011 .

[17]  W. Geisler,et al.  Contributions of ideal observer theory to vision research , 2011, Vision Research.

[18]  Jonathan Winawer,et al.  Imaging retinotopic maps in the human brain , 2011, Vision Research.

[19]  Farran Briggs,et al.  Corticogeniculate feedback and visual processing in the primate , 2011, The Journal of physiology.

[20]  Nicole C. Rust,et al.  Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT , 2010, The Journal of Neuroscience.

[21]  D. Mumford,et al.  Pattern Theory: The Stochastic Analysis of Real-World Signals , 2010 .

[22]  N. Kanwisher.  Functional specificity in the human brain: A window into the functional architecture of the mind , 2010, Proceedings of the National Academy of Sciences.

[23]  Aapo Hyvärinen,et al.  Statistical Models of Natural Images and Cortical Visual Representation , 2010, Top. Cogn. Sci..

[24]  R. McIntosh,et al.  Do we have independent visual streams for perception and action? , 2010, Cognitive neuroscience.

[25]  Miguel P. Eckstein,et al.  Learning motion: Human vs. optimal Bayesian learner , 2010, Vision Research.

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

[27]  Alan L. Yuille.  Belief Propagation , Mean-field , and Bethe approximations , 2010 .

[28]  Tai Sing Lee,et al.  Cooperative and Competitive Interactions Facilitate Stereo Computations in Macaque Primary Visual Cortex , 2009, The Journal of Neuroscience.

[29]  Jonathan S. Cant,et al.  Living in a material world: how visual cues to material properties affect the way that we lift objects and perceive their weight. , 2009, Journal of neurophysiology.

[30]  Aniruddha Das,et al.  Optical imaging of contextual interactions in V1 of the behaving monkey. , 2009, Journal of neurophysiology.

[31]  P. Dayan,et al.  Perceptual organization in the tilt illusion. , 2009, Journal of vision.

[32]  D. Kersten,et al.  Border Ownership Selectivity in Human Early Visual Cortex and its Modulation by Attention , 2009, The Journal of Neuroscience.

[33]  Jeffrey S. Perry,et al.  Contour statistics in natural images: Grouping across occlusions , 2009, Visual Neuroscience.

[34]  A. Roe,et al.  Visual System: Functional Architecture of Area V2 , 2009 .

[35]  Doris Y. Tsao,et al.  Comparing face patch systems in macaques and humans , 2008, Proceedings of the National Academy of Sciences.

[36]  Richard S. Zemel,et al.  Characterizing response behavior in multisensory perception with conflicting cues , 2008, NIPS.

[37]  D. Bavelier,et al.  Exercising your brain: a review of human brain plasticity and training-induced learning. , 2008, Psychology and aging.

[38]  J. Movshon,et al.  Structure and Function Come Unglued in the Visual Cortex , 2008, Neuron.

[39]  Shimon Ullman,et al.  Image interpretation by a single bottom-up top-down cycle , 2008, Proceedings of the National Academy of Sciences.

[40]  R. Sundareswara,et al.  Perceptual multistability predicted by search model for Bayesian decisions. , 2008, Journal of vision.

[41]  David C. Burr,et al.  Young Children Do Not Integrate Visual and Haptic Form Information , 2008, Current Biology.

[42]  Thomas L. Griffiths,et al.  A Primer on Probabilistic Inference , 2008 .

[43]  Anna W Roe,et al.  Optical imaging of contrast response in Macaque monkey V1 and V2. , 2007, Cerebral cortex.

[44]  B. Wandell,et al.  Visual Field Maps in Human Cortex , 2007, Neuron.

[45]  Konrad Paul Kording,et al.  Causal Inference in Multisensory Perception , 2007, PloS one.

[46]  Paul R. Martin,et al.  The unsolved mystery of vision , 2007, Current Biology.

[47]  J. Rieser,et al.  Bayesian integration of spatial information. , 2007, Psychological bulletin.

[48]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

[49]  Stuart Geman,et al.  Invariance and selectivity in the ventral visual pathway , 2006, Journal of Physiology-Paris.

[50]  Tai Sing Lee,et al.  Efficient Coding of Visual Scenes by Grouping and Segmentation: Theoretical Predictions and Biological Evidence , 2006 .

[51]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[52]  E. Callaway,et al.  The Parvocellular LGN Provides a Robust Disynaptic Input to the Visual Motion Area MT , 2006, Neuron.

[53]  H. Komatsu.  The neural mechanisms of perceptual filling-in , 2006, Nature Reviews Neuroscience.

[54]  S. Grossberg,et al.  A neural model of surface perception: lightness, anchoring, and filling-in. , 2006, Spatial vision.

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

[56]  Permalink.  Title Efficient Coding of Visual Scenes by Grouping and Segmentation : Theoretical Predictions and Biological Evidence , 2005 .

[57]  Takeo Watanabe,et al.  The primary visual cortex fills in color , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[58]  M. Wallace,et al.  Unifying multisensory signals across time and space , 2004, Experimental Brain Research.

[59]  D. M. Kammen,et al.  Quadrature and the development of orientation selective cortical cells by Hebb rules , 1989, Biological Cybernetics.

[60]  A. Yuille,et al.  Energy functions for early vision and analog networks , 1989, Biological Cybernetics.

[61]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[62]  David Mumford,et al.  On the computational architecture of the neocortex , 2004, Biological Cybernetics.

[63]  R. Zemel,et al.  Inference and computation with population codes. , 2003, Annual review of neuroscience.

[64]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[65]  Daniel Kersten,et al.  Bayesian models of object perception , 2003, Current Opinion in Neurobiology.

[66]  David C Knill,et al.  Mixture models and the probabilistic structure of depth cues , 2003, Vision Research.

[67]  Alan L. Yuille,et al.  Statistical Edge Detection: Learning and Evaluating Edge Cues , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[68]  R W Guillery,et al.  The role of the thalamus in the flow of information to the cortex. , 2002, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

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

[70]  J. Elder,et al.  Ecological statistics of Gestalt laws for the perceptual organization of contours. , 2002, Journal of vision.

[71]  Edward H. Adelson,et al.  Motion illusions as optimal percepts , 2002, Nature Neuroscience.

[72]  D. Ringach,et al.  On the classification of simple and complex cells , 2002, Vision Research.

[73]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[74]  Rüdiger von der Heydt,et al.  Image Parsing Mechanisms of the Visual Cortex , 2002 .

[75]  Ronen Basri,et al.  Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[76]  Jan J. Koenderink,et al.  On the Affine Structure of Perceptual Space , 2001, Psychological science.

[77]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[78]  T. S. Lee,et al.  Dynamics of subjective contour formation in the early visual cortex. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Refractor.  Vision , 2000, The Lancet.

[80]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[81]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[82]  D. Knill.  Contour into texture: information content of surface contours and texture flow. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[83]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

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

[85]  Ronald M. Lesperance,et al.  The Gaussian derivative model for spatial-temporal vision: I. Cortical model. , 2001, Spatial vision.

[86]  Wolfgang Maass,et al.  On the Computational Power of Winner-Take-All , 2000, Neural Computation.

[87]  C. Gilbert,et al.  Spatial distribution of contextual interactions in primary visual cortex and in visual perception. , 2000, Journal of neurophysiology.

[88]  R. von der Heydt,et al.  Coding of Border Ownership in Monkey Visual Cortex , 2000, The Journal of Neuroscience.

[89]  Alan L. Yuille,et al.  Statistical cues for domain specific image segmentation with performance analysis , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[90]  Paul R. Schrater,et al.  Mechanisms of visual motion detection , 2000, Nature Neuroscience.

[91]  A. Hurlbert,et al.  Perception of three-dimensional shape influences colour perception through mutual illumination , 1999, Nature.

[92]  Martin J. Wainwright,et al.  Scale Mixtures of Gaussians and the Statistics of Natural Images , 1999, NIPS.

[93]  The Cradle of Knowledge: Development of Perception in Infancy, by Philip J. Kellman and Martha E. Arterberry , 1999, Trends in Cognitive Sciences.

[94]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[95]  R. Jacobs,et al.  Optimal integration of texture and motion cues to depth , 1999, Vision Research.

[96]  Michael J. Berry,et al.  The Neural Code of the Retina , 1999, Neuron.

[97]  A. Borst.  Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[98]  P. Lennie.  Single Units and Visual Cortical Organization , 1998, Perception.

[99]  Bartlett W. Mel,et al.  Translation-Invariant Orientation Tuning in Visual “Complex” Cells Could Derive from Intradendritic Computations , 1998, The Journal of Neuroscience.

[100]  E. Adelson,et al.  Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision , 1998 .

[101]  Alan L. Yuille,et al.  Visual motion estimation and prediction: a probabilistic network model for temporal coherence , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[102]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[103]  Pamela Reinagel,et al.  Decoding visual information from a population of retinal ganglion cells. , 1997, Journal of neurophysiology.

[104]  D Kersten,et al.  Moving Cast Shadows Induce Apparent Motion in Depth , 1997, Perception.

[105]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[106]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[107]  A. Yuille,et al.  Bayesian decision theory and psychophysics , 1996 .

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

[109]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

[110]  David L. Sheinberg,et al.  Visual object recognition. , 1996, Annual review of neuroscience.

[111]  Wendy L. Braje,et al.  Human efficiency for recognizing 3-D objects in luminance noise , 1995, Vision Research.

[112]  Jon H. Kaas,et al.  The emergence and evolution of mammalian neocortex , 1995, Trends in Neurosciences.

[113]  Victor A. F. Lamme.  The neurophysiology of figure-ground segregation in primary visual cortex , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[114]  M. Landy,et al.  Measurement and modeling of depth cue combination: in defense of weak fusion , 1995, Vision Research.

[115]  S. McKee,et al.  Detecting a trajectory embedded in random-direction motion noise , 1995, Vision Research.

[116]  S Ullman,et al.  Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex. , 1995, Cerebral cortex.

[117]  M. Goodale,et al.  The visual brain in action , 1995 .

[118]  Ning Qian,et al.  Computing Stereo Disparity and Motion with Known Binocular Cell Properties , 1994, Neural Computation.

[119]  William T. Freeman,et al.  The generic viewpoint assumption in a framework for visual perception , 1994, Nature.

[120]  A. Parker,et al.  Effects of different texture cues on curved surfaces viewed stereoscopically , 1993, Vision Research.

[121]  John H. R. Maunsell,et al.  How parallel are the primate visual pathways? , 1993, Annual review of neuroscience.

[122]  D. Heeger.  Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[123]  Bennett L. Schwartz,et al.  Interaction between Transparency and Structure from Motion , 1992, Neural Computation.

[124]  Tai Sing Lee,et al.  Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model , 1992, ECCV.

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

[126]  D C Van Essen,et al.  Information processing in the primate visual system: an integrated systems perspective. , 1992, Science.

[127]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[128]  D. Knill,et al.  Apparent surface curvature affects lightness perception , 1991, Nature.

[129]  J T McIlwain,et al.  Distributed spatial coding in the superior colliculus: A review , 1991, Visual Neuroscience.

[130]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[131]  T. Wiesel,et al.  The influence of contextual stimuli on the orientation selectivity of cells in primary visual cortex of the cat , 1990, Vision Research.

[132]  James J. Clark,et al.  Data Fusion for Sensory Information Processing Systems , 1990 .

[133]  I. Ohzawa,et al.  Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. , 1990, Science.

[134]  A. Yuille,et al.  A common framework for image segmentation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[135]  Heinrich H. Bülthoff,et al.  Integration of Stereo, Shading and Texture , 1990 .

[136]  A. Yuille,et al.  A model for the estimate of local image velocity by cells in the visual cortex , 1990, Proceedings of the Royal Society of London. B. Biological Sciences.

[137]  Richard Durbin,et al.  A dimension reduction framework for understanding cortical maps , 1990, Nature.

[138]  H. Bülthoff,et al.  Does the brain know the physics of specular reflection? , 1990, Nature.

[139]  D. Pelli.  The quantum efficiency of vision , 1990 .

[140]  L. Abbott,et al.  Model neurons: From Hodgkin-Huxley to hopfield , 1990 .

[141]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[142]  E. Reed.  The Ecological Approach to Visual Perception , 1989 .

[143]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[144]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[145]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[146]  Daniel J. Amit,et al.  Modeling brain function: the world of attractor neural networks, 1st Edition , 1989 .

[147]  D. Burr,et al.  Feature detection in human vision: a phase-dependent energy model , 1988, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[148]  H H Bülthoff,et al.  Integration of depth modules: stereo and shading. , 1988, Journal of the Optical Society of America. A, Optics and image science.

[149]  Tomaso Poggio,et al.  Cooperative computation of stereo disparity , 1988 .

[150]  V. Ramachandran,et al.  Visual inertia in apparent motion , 1987, Vision Research.

[151]  D Kersten,et al.  Predictability and redundancy of natural images. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[152]  J. P. Jones,et al.  The two-dimensional spatial structure of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[153]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation-selective cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[154]  R Linsker,et al.  From basic network principles to neural architecture: emergence of spatial-opponent cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[155]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[156]  C Koch,et al.  Analog "neuronal" networks in early vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[157]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[158]  S. Grossberg,et al.  Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading. , 1985, Psychological review.

[159]  S Grossberg,et al.  Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations , 1985, Perception & psychophysics.

[160]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[161]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[162]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[163]  R. von der Heydt,et al.  Illusory contours and cortical neuron responses. , 1984, Science.

[164]  E. Hildreth.  The computation of the velocity field , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[165]  D. Hubel,et al.  Anatomy and physiology of a color system in the primate visual cortex , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[166]  H. B. Barlow,et al.  What does the eye see best? , 1983, Nature.

[167]  John F. Kalaska,et al.  Spatial coding of movement: A hypothesis concerning the coding of movement direction by motor cortical populations , 1983 .

[168]  D. Hubel.  Evolution of ideas on the primary visual cortex, 1955–1978: A biased historical account , 1982, Bioscience reports.

[169]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[170]  D. Pollen,et al.  Phase relationships between adjacent simple cells in the visual cortex. , 1981, Science.

[171]  Eric L. Schwartz,et al.  Computational anatomy and functional architecture of striate cortex: A spatial mapping approach to perceptual coding , 1980, Vision Research.

[172]  S. Ullman,et al.  The Interpretation of Visual Motion , 1981 .

[173]  Ulf Grenander.  Pattern synthesis , 1976, Lectures in pattern theory / U. Grenander.

[174]  Parvati Dev,et al.  Perception of Depth Surfaces in Random-Dot Stereograms: A Neural Model , 1975, Int. J. Man Mach. Stud..

[175]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[176]  B. Julesz.  Foundations of Cyclopean Perception , 1971 .

[177]  S. Grossberg,et al.  Some physiological and biochemical consequences of psychological postulates. , 1968, Proceedings of the National Academy of Sciences of the United States of America.

[178]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[179]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[180]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[181]  H B Barlow,et al.  Measurements of the quantum efficiency of discrimination in human scotopic vision , 1962, The Journal of physiology.

[182]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[183]  W. Pitts,et al.  What the Frog's Eye Tells the Frog's Brain , 1959, Proceedings of the IRE.