Information Routing, Correspondence Finding, and Object Recognition in the Brain

Background and Concepts.- A Correspondence-Based Neural Model for Face Recognition.- Switchyards-Routing Structures in the Brain.- Ontogenesis of Switchyards.- Putting the Pieces Together: Recognition with Switchyards.- Discussion and Outlook.

[1]  C. Cherniak The Bounded Brain: Toward Quantitative Neuroanatomy , 1990, Journal of Cognitive Neuroscience.

[2]  Kevan A. C. Martin,et al.  A Canonical Microcircuit for Neocortex , 1989, Neural Computation.

[3]  J. R. Lee,et al.  How Does the Striate Cortex Begin the Reconstruction of the Visual World? , 1971, Science.

[4]  David S. Greenberg,et al.  Population imaging of ongoing neuronal activity in the visual cortex of awake rats , 2008, Nature Neuroscience.

[5]  David E. Rumelhart,et al.  Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks , 1989, Neural Computation.

[6]  Heiko Wersing,et al.  Learning Optimized Features for Hierarchical Models of Invariant Object Recognition , 2003, Neural Computation.

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

[8]  B. Olshausen Neural routing circuits for forming invariant representations of visual objects , 1994 .

[9]  Bruno A. Olshausen,et al.  Pattern recognition, attention, and information bottlenecks in the primate visual system , 1991, Defense, Security, and Sensing.

[10]  A. Treisman,et al.  Conjunction search revisited. , 1990, Journal of experimental psychology. Human perception and performance.

[11]  Cornelius Weber,et al.  DEVELOPMENT AND REGENERATION OF THE RETINOTECTAL MAP IN GOLDFISH : A COMPUTATIONAL STUDY , 1997 .

[12]  Eric L. Schwartz,et al.  Computing with the Leaky Integrate-and-Fire Neuron: Logarithmic Computation and Multiplication , 1997, Neural Computation.

[13]  Andreas T. Schaefer,et al.  Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. , 2003, Journal of neurophysiology.

[14]  J. Schmidt,et al.  Activity-driven sharpening of the retinotectal projection in goldfish: development under stroboscopic illumination prevents sharpening. , 1993, Journal of neurobiology.

[15]  Andrew Zisserman,et al.  Scene Classification Using a Hybrid Generative/Discriminative Approach , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  D. Wang,et al.  The time dimension for scene analysis , 2005, IEEE Transactions on Neural Networks.

[17]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[18]  I. Fujita,et al.  Organization of horizontal axons in the inferior temporal cortex and primary visual cortex of the macaque monkey. , 2005, Cerebral cortex.

[19]  G. Elston,et al.  Morphological variation of layer III pyramidal neurones in the occipitotemporal pathway of the macaque monkey visual cortex. , 1998, Cerebral cortex.

[20]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[21]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[22]  Haim Sompolinsky,et al.  Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.

[23]  R. Malenka,et al.  Dopaminergic modulation of neuronal excitability in the striatum and nucleus accumbens. , 2000, Annual review of neuroscience.

[24]  Elie Bienenstock,et al.  A neural network for invariant pattern recognition. , 1987 .

[25]  W. Pitts,et al.  How we know universals; the perception of auditory and visual forms. , 1947, The Bulletin of mathematical biophysics.

[26]  Christoph von der Malsburg,et al.  Maplets for correspondence-based object recognition , 2004, Neural Networks.

[27]  M. A. Repucci,et al.  Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex , 2002 .

[28]  Cordelia Schmid,et al.  Affine-invariant local descriptors and neighborhood statistics for texture recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[29]  D. Buxhoeveden,et al.  The minicolumn hypothesis in neuroscience. , 2002, Brain : a journal of neurology.

[30]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[31]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Pietro Perona,et al.  A Bayesian approach to unsupervised one-shot learning of object categories , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[33]  Vivien A. Casagrande,et al.  Biophysics of Computation: Information Processing in Single Neurons , 1999 .

[34]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[35]  Klaus Obermayer,et al.  Singularities in Primate Orientation Maps , 1997, Neural Computation.

[36]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[37]  K. Rockland,et al.  Some thoughts on cortical minicolumns , 2004, Experimental Brain Research.

[38]  D. Kersten,et al.  The representation of perceived angular size in human primary visual cortex , 2006, Nature Neuroscience.

[39]  Rolf P. Würtz,et al.  Multilayer dynamic link networks for establishing image point correspondences and visual object recognition , 1995 .

[40]  Nicolas Pinto,et al.  Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..

[41]  Irving Biederman,et al.  Invariance of long-term visual priming to scale, reflection, translation, and hemisphere , 2001, Vision Research.

[42]  J. B. Levitt,et al.  Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex. , 1993, Cerebral cortex.

[43]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[44]  David I. Perrett,et al.  Modeling visual recognition from neurobiological constraints , 1994, Neural Networks.

[45]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[46]  R. Sperry CHEMOAFFINITY IN THE ORDERLY GROWTH OF NERVE FIBER PATTERNS AND CONNECTIONS. , 1963, Proceedings of the National Academy of Sciences of the United States of America.

[47]  C. B. Shelman,et al.  Morphology of the primate optic nerve. I. Method and total fiber count. , 1972, Investigative ophthalmology.

[48]  J. Cowan,et al.  A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.

[49]  R Lawson,et al.  The effect of prior experience on recognition thresholds for plane-disoriented pictures of familiar objects , 1999, Memory & cognition.

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

[51]  X. Jin Factor graphs and the Sum-Product Algorithm , 2002 .

[52]  F. Allgower,et al.  Complexity reduction of a thin film deposition model using a trajectory based nonlinear model reduction technique , 2005, Proceedings of the 2005, American Control Conference, 2005..

[53]  J. Daugman Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[54]  H. Wässle,et al.  The cat optic nerve: Fibre total count and diameter spectrum , 2004, The Journal of comparative neurology.

[55]  J R Duhamel,et al.  The updating of the representation of visual space in parietal cortex by intended eye movements. , 1992, Science.

[56]  Stefan Wermter,et al.  A self-organizing map of sigma-pi units , 2007, Neurocomputing.

[57]  Eric O. Postma,et al.  SCAN: A Scalable Model of Attentional Selection , 1997, Neural Networks.

[58]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[59]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[60]  N. Kanwisher,et al.  The fusiform face area: a cortical region specialized for the perception of faces , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[61]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[62]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[63]  I. Gauthier,et al.  Expertise for cars and birds recruits brain areas involved in face recognition , 2000, Nature Neuroscience.

[64]  E. G. Jones,et al.  Synapses of double bouquet cells in monkey cerebral cortex visualized by calbindin immunoreactivity , 1989, Brain Research.

[65]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[66]  M. Tarr,et al.  FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise , 2000, Nature Neuroscience.

[67]  I. Biederman,et al.  Localizing the cortical region mediating visual awareness of object identity. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[68]  Ken Nakayama,et al.  Serial and parallel processing of visual feature conjunctions , 1986, Nature.

[69]  Richard A. Andersen,et al.  A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.

[70]  Marc-Oliver Gewaltig,et al.  A model of computation in neocortical architecture , 1999, Neural Networks.

[71]  E. Rolls,et al.  A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.

[72]  J. Tenenbaum,et al.  Special issue on “Probabilistic models of cognition , 2022 .

[73]  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 .

[74]  G. Elston,et al.  Pyramidal Cells, Patches, and Cortical Columns: a Comparative Study of Infragranular Neurons in TEO, TE, and the Superior Temporal Polysensory Area of the Macaque Monkey , 2000, The Journal of Neuroscience.

[75]  R. Desimone,et al.  Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. , 1997, Journal of neurophysiology.

[76]  Christoph von der Malsburg,et al.  A Marker-Based Model for the Ontogenesis of Routing Circuits , 2007, ICANN.

[77]  Antonio Torralba,et al.  Context-based vision system for place and object recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[78]  Keiji Tanaka Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities. , 2003, Cerebral cortex.

[79]  Doris Y. Tsao,et al.  A Cortical Region Consisting Entirely of Face-Selective Cells , 2006, Science.

[80]  A. Peters,et al.  The organization of pyramidal cells in area 18 of the rhesus monkey. , 1997, Cerebral cortex.

[81]  C. Koch,et al.  Multiplicative computation in a visual neuron sensitive to looming , 2002, Nature.

[82]  R. Desimone,et al.  Attentional control of visual perception: cortical and subcortical mechanisms. , 1990, Cold Spring Harbor symposia on quantitative biology.

[83]  Christian Wolff,et al.  Dynamic Link Matching between Feature Columns for Different Scale and Orientation , 2007, ICONIP.

[84]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[85]  D. V. van Essen,et al.  Responses in area V4 depend on the spatial relationship between stimulus and attention. , 1996, Journal of neurophysiology.

[86]  H. Kennedy,et al.  Topography of the afferent connectivity of area 17 in the macaque monkey: A double‐labelling study , 1986, The Journal of comparative neurology.

[87]  Bartlett W. Mel,et al.  Minimizing Binding Errors Using Learned Conjunctive Features , 2000, Neural Computation.

[88]  Jennifer A. Mangels,et al.  Predictive Codes for Forthcoming Perception in the Frontal Cortex , 2006, Science.

[89]  B. Fischer,et al.  Visual field representations and locations of visual areas V1/2/3 in human visual cortex. , 2003, Journal of vision.

[90]  Wiel H. Janssen,et al.  Evaluation studies , 1993, Generic Intelligent Driver Support.

[91]  David W. Arathorn,et al.  Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision , 2002 .

[92]  G. Rager,et al.  Generation and degeneration of retinal ganglion cells in the chicken , 1976, Experimental Brain Research.

[93]  Ronald A. Rensink,et al.  Change blindness: past, present, and future , 2005, Trends in Cognitive Sciences.

[94]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[95]  T. Sejnowski,et al.  Book Review: Gain Modulation in the Central Nervous System: Where Behavior, Neurophysiology, and Computation Meet , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[96]  A. Peters,et al.  Neuronal organization in area 17 of cat visual cortex. , 1993, Cerebral cortex.

[97]  I Biederman,et al.  Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[98]  T. Womelsdorf,et al.  Dynamic shifts of visual receptive fields in cortical area MT by spatial attention , 2006, Nature Neuroscience.

[99]  R. Douglas,et al.  Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.

[100]  A. Schmitt,et al.  Wnt–Ryk signalling mediates medial–lateral retinotectal topographic mapping , 2006, Nature.

[101]  Jörg Lücke,et al.  Invariant Face Recognitionin a Network of Cortical Columns , 2008, VISAPP.

[102]  E. Callaway,et al.  Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.

[103]  Cristina Savin,et al.  Resonance or integration? Self-sustained dynamics and excitability of neural microcircuits. , 2007, Journal of neurophysiology.

[104]  D J Willshaw,et al.  A marker induction mechanism for the establishment of ordered neural mappings: its application to the retinotectal problem. , 1979, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[105]  D C Van Essen,et al.  Shifter circuits: a computational strategy for dynamic aspects of visual processing. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[106]  S. Kastner,et al.  Two hierarchically organized neural systems for object information in human visual cortex , 2008, Nature Neuroscience.

[107]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[108]  J. Orbach Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[109]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[110]  R. Meyer,et al.  The effect of TTX‐activity blockade and total darkness on the formation of retinotopy in the goldfish retinotectal projection , 1991, The Journal of comparative neurology.

[111]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[112]  Jörg Lücke,et al.  Rapid Convergence to Feature Layer Correspondences , 2008, Neural Computation.

[113]  Bruno A Olshausen,et al.  Timecourse of neural signatures of object recognition. , 2003, Journal of vision.

[114]  M. Eigen Selforganization of matter and the evolution of biological macromolecules , 1971, Naturwissenschaften.

[115]  Glyn W. Humphreys Dietmar Heinke,et al.  Spatial Representation and Selection in the Brain: Neuropsychological and Computational Constraints , 1998 .

[116]  F. Bremmer,et al.  Spatial invariance of visual receptive fields in parietal cortex neurons , 1997, Nature.

[117]  C. Holt,et al.  Position, guidance, and mapping in the developing visual system. , 1993, Journal of neurobiology.

[118]  János Komlós,et al.  An 0(n log n) sorting network , 1983, STOC.

[119]  Christoph von der Malsburg,et al.  What Is the Optimal Architecture for Visual Information Routing? , 2007, Neural Computation.

[120]  E. L. Schwartz,et al.  Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception , 1977, Biological Cybernetics.

[121]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.

[122]  Yasuomi D. Sato,et al.  A Visual Object Recognition System Invariant to Scale and Rotation , 2008, ICANN.

[123]  J. Duncan Selective attention and the organization of visual information. , 1984, Journal of experimental psychology. General.

[124]  W. Singer,et al.  Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[125]  LinLin Shen,et al.  Face authentication test on the BANCA database , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[126]  David D. Cox,et al.  'Breaking' position-invariant object recognition , 2005, Nature Neuroscience.

[127]  Jerome A. Feldman,et al.  Dynamic connections in neural networks , 1990, Biological Cybernetics.

[128]  Joseph F. Murray,et al.  Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning , 2007, Neural Computation.

[129]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[130]  Rolf P. Würtz,et al.  Image Representation by Complex Cell Responses , 2004, Neural Computation.

[131]  M. Goldberg,et al.  The time course of perisaccadic receptive field shifts in the lateral intraparietal area of the monkey. , 2003, Journal of neurophysiology.

[132]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[133]  Christian Wolff,et al.  A recurrent dynamic model for correspondence-based face recognition. , 2008, Journal of vision.

[134]  B. Sakmann,et al.  A new cellular mechanism for coupling inputs arriving at different cortical layers , 1999, Nature.

[135]  Thomas L. Thornton,et al.  Parallel and serial processes in visual search. , 2007, Psychological review.

[136]  Satoru Kondo,et al.  Neocortical Inhibitory Terminals Innervate Dendritic Spines Targeted by Thalamocortical Afferents , 2007, The Journal of Neuroscience.

[137]  H. A.F.,et al.  DEVELOPMENT OF RETINOTOPIC PROJECTIONS: AN ANALYTIC TREATMENT , 1983 .

[138]  E. Callaway,et al.  Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons , 2000, Nature Neuroscience.

[139]  Laurenz Wiskott,et al.  The role of topographical constraints in face recognition , 1999, Pattern Recognition Letters.

[140]  T. Sejnowski,et al.  Spatial Transformations in the Parietal Cortex Using Basis Functions , 1997, Journal of Cognitive Neuroscience.

[141]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[142]  M. Fahle,et al.  Limited translation invariance of human visual pattern recognition , 1998, Perception & psychophysics.

[143]  V. Mountcastle The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.

[144]  Christoph von der Malsburg,et al.  Attentional Processes in Correspondence-Based Object Recognition , 2008 .

[145]  Jörg Lücke,et al.  Rapid Processing and Unsupervised Learning in a Model of the Cortical Macrocolumn , 2004, Neural Computation.

[146]  Takayuki Ito,et al.  Neocognitron: A neural network model for a mechanism of visual pattern recognition , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[147]  J. Powell,et al.  Efferent synaptic connections of grafted dopaminergic neurons reinnervating the host neostriatum: a tyrosine hydroxylase immunocytochemical study , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[148]  E. G. Jones,et al.  Microcolumns in the cerebral cortex. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[149]  Laurenz Wiskott,et al.  Face recognition by dynamic link matching , 1996 .

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

[151]  G. Goodhill,et al.  A new chemotaxis assay shows the extreme sensitivity of axons to molecular gradients , 2004, Nature Neuroscience.

[152]  D. V. van Essen,et al.  A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[153]  Wulfram Gerstner,et al.  Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking , 2000, Neural Computation.

[154]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[155]  Y. Fukuda,et al.  Strain differences in quantitative analysis of the rat optic nerve , 1982, Experimental Neurology.

[156]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[157]  C. Bundesen,et al.  Visual transformation of size. , 1975, Journal of experimental psychology. Human perception and performance.

[158]  R. Linsker From basic network principles to neural architecture (series) , 1986 .

[159]  M. Diamond,et al.  Demonstration of discrete place‐defined columns—segregates—in the cat SI , 1990, The Journal of comparative neurology.

[160]  E. Welker,et al.  Recovery of evoked potentials, metabolic activity and behavior in a mouse model of somatosensory cortex lesion: role of the neural cell adhesion molecule (NCAM). , 2004, Cerebral cortex.

[161]  Trichur Raman Vidyasagar,et al.  A linear model fails to predict orientation selectivity of cells in the cat visual cortex. , 1996, The Journal of physiology.

[162]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[163]  Bartlett W. Mel SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[164]  Andy Adler,et al.  Comparing Human and Automatic Face Recognition Performance , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[165]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[166]  B. Renault,et al.  ERPs and chronometry of face recognition: following‐up Seeck et al. and George et al. , 1998, Neuroreport.