Contour integration and segmentation with self-organized lateral connections

Abstract.Contour integration in low-level vision is believed to occur based on lateral interaction between neurons with similar orientation tuning. How such interactions could arise in the brain has been an open question. Our model suggests that the interactions can be learned through input-driven self-organization, i.e., through the same mechanism that underlies many other developmental and functional phenomena in the visual cortex. The model also shows how synchronized firing mediated by these lateral connections can represent the percept of a contour, resulting in performance similar to that of human contour integration. The model further demonstrates that contour integration performance can differ in different parts of the visual field, depending on what kinds of input distributions they receive during development. The model thus grounds an important perceptual phenomenon onto detailed neural mechanisms so that various structural and functional properties can be measured and predictions can be made to guide future experiments.

[1]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[2]  F. Crick Function of the thalamic reticular complex: the searchlight hypothesis. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[3]  G. Blasdel,et al.  Voltage-sensitive dyes reveal a modular organization in monkey striate cortex , 1986, Nature.

[4]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[5]  H. Tamura,et al.  Inhibition contributes to orientation selectivity in visual cortex of cat , 1988, Nature.

[6]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[7]  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.

[8]  H. Ritter,et al.  A principle for the formation of the spatial structure of cortical feature maps. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Reinhard Eckhorn,et al.  Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[10]  F. Previc Functional specialization in the lower and upper visual fields in humans: Its ecological origins and neurophysiological implications , 1990, Behavioral and Brain Sciences.

[11]  P König,et al.  Synchronization of oscillatory neuronal responses between striate and extrastriate visual cortical areas of the cat. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[12]  C. Gilbert,et al.  Synaptic physiology of horizontal connections in the cat's visual cortex , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  G. Blasdel,et al.  Orientation selectivity, preference, and continuity in monkey striate cortex , 1992, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  L C Katz,et al.  Development of local circuits in mammalian visual cortex. , 1992, Annual review of neuroscience.

[15]  Wolfgang Rosenstiel,et al.  COKOS: A COprocessor for KOhonen’s Selforganizing Map , 1993 .

[16]  Herbert J. Reitboeck,et al.  Object separation in dynamic neural networks , 1993, IEEE International Conference on Neural Networks.

[17]  Teuvo Kohonen,et al.  Physiological interpretationm of the self-organizing map algorithm , 1993 .

[18]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[19]  W. Singer Synchronization of cortical activity and its putative role in information processing and learning. , 1993, Annual review of physiology.

[20]  Teuvo Kohonen,et al.  Physiological interpretation of the Self-Organizing Map algorithm , 1993, Neural Networks.

[21]  R. Frostig,et al.  Cortical point-spread function and long-range lateral interactions revealed by real-time optical imaging of macaque monkey primary visual cortex , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[22]  Stephen Grossberg,et al.  Physiological Interpretation of the Self-Organizing Map Algorithm , 1994 .

[23]  KD Miller A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[25]  D. Fitzpatrick,et al.  Patterns of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns , 1995, Neuron.

[26]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[27]  Deliang Wang,et al.  Global competition and local cooperation in a network of neural oscillators , 1995 .

[28]  DeLiang Wang,et al.  Emergent synchrony in locally coupled neural oscillators , 1995, IEEE Trans. Neural Networks.

[29]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[30]  N. Swindale The development of topography in the visual cortex: a review of models. , 1996 .

[31]  K. Nakayama,et al.  Enhanced Perception of Illusory Contours in the Lower Versus Upper Visual Hemifields , 1996, Science.

[32]  K. Kirschfeld The temporal-correlation hypothesis. , 1996, Trends in neurosciences.

[33]  M. Sur,et al.  Orientation Maps of Subjective Contours in Visual Cortex , 1996, Science.

[34]  N. Swindale The development of topography in the visual cortex: a review of models. , 1996, Network.

[35]  R. Miikkulainen,et al.  Self-Organization, Plasticity, and Low-Level Visual Phenomena in a Laterally Connected Map Model of the Primary Visual Cortex , 1997 .

[36]  Risto Miikkulainen,et al.  Topographic Receptive Fields and Patterned Lateral Interaction in a Self-Organizing Model of the Primary Visual Cortex , 1997, Neural Computation.

[37]  Risto Miikkulainen,et al.  Self-Organization and Segmentation with Laterally Connected Spiking Neurons , 1997, IJCAI.

[38]  Steven C. Dakin,et al.  Absence of contour linking in peripheral vision , 1997, Nature.

[39]  Leif H. Finkel,et al.  Identification of salient contours in cluttered images , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[40]  D. Fitzpatrick,et al.  Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex , 1997, The Journal of Neuroscience.

[41]  S. Grossberg,et al.  Cortical Synchronization and Perceptual Framing , 1997, Journal of Cognitive Neuroscience.

[42]  Risto Miikkulainen,et al.  A Self-Organizing Neural Network Model of the Primary Visual Cortex , 1998, ICONIP.

[43]  Wolfgang Maass,et al.  Spiking Neurons , 1998, NC.

[44]  Risto Miikkulainen,et al.  Pattern-generator-driven development in self-organizing models , 1998 .

[45]  L. Finkel,et al.  Extraction of perceptually salient contours by striate cortical networks , 1998, Vision Research.

[46]  Risto Miikkulainen,et al.  Self-organization and segmentation in a laterally connected orientation map of spiking neurons , 1998, Neurocomputing.

[47]  Zhaoping Li,et al.  A Neural Model of Contour Integration in the Primary Visual Cortex , 1998, Neural Computation.

[48]  Suzanne P. McKee,et al.  Constraints on long range interactions mediating contour detection , 1998, Vision Research.

[49]  DeLiang Wang,et al.  Synchrony and Desynchrony in Integrate-and-Fire Oscillators , 1999, Neural Computation.

[50]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[51]  P Reinagel,et al.  Natural scene statistics at the centre of gaze. , 1999, Network.

[52]  C. Gray The Temporal Correlation Hypothesis of Visual Feature Integration Still Alive and Well , 1999, Neuron.

[53]  E W Lang,et al.  An Incremental Hebbian Learning Model of the Primary Visual Cortex with Lateral Plasticity and Real Input Patterns , 1999, Zeitschrift fur Naturforschung. C, Journal of biosciences.

[54]  Risto Miikkulainen,et al.  Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex , 2000, Neural Computation.

[55]  DeLiang Wang,et al.  On Connectedness: A Solution Based on Oscillatory Correlation , 2000, Neural Computation.

[56]  Stephen Grossberg,et al.  Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps , 2000, Neural Networks.

[57]  D. P. Gallogly,et al.  Image Structure Models of Texture and Contour Visibility , 2000 .

[58]  C. Gray,et al.  Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[59]  Risto Miikkulainen,et al.  Self-Organization of Innate Face Preferences: Could Genetics Be Expressed through Learning? , 2000, AAAI/IAAI.

[60]  G. Goodhill,et al.  Analysis of the elastic net model applied to the formation of ocular dominance and orientation columns. , 2000, Network.

[61]  Arnaud Delorme,et al.  Feed-forward contour integration in primary visual cortex based on asynchronous spike propagation , 2001, Neurocomputing.

[62]  S. Grossberg,et al.  A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual grouping and learning. , 2010, Cerebral cortex.

[63]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[64]  Risto Miikkulainen,et al.  Perceptual groupings in a self-organizing map of spiking neurons , 2001 .

[65]  J. Leo van Hemmen,et al.  Combined Hebbian development of geniculocortical and lateral connectivity in a model of primary visual cortex , 2001, Biological Cybernetics.

[66]  L. Maffei,et al.  Neurotrophins and plasticity in the visual cortex. , 2002, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[67]  L. Maffei,et al.  Book Review: Neurotrophins and Plasticity in the Visual Cortex , 2002 .

[68]  R. Miikkulainen,et al.  Learning to see: genetic and environmental influences on visual development , 2002 .

[69]  Risto Miikkulainen,et al.  Modeling large cortical networks with growing self-organizing maps , 2002, Neurocomputing.

[70]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[71]  Ch. von der Malsburg,et al.  A neural cocktail-party processor , 1986, Biological Cybernetics.

[72]  R. Eckhorn,et al.  Coherent oscillations: A mechanism of feature linking in the visual cortex? , 1988, Biological Cybernetics.

[73]  C. Malsburg Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[74]  Risto Miikkulainen,et al.  Cooperative self-organization of afferent and lateral connections in cortical maps , 1994, Biological Cybernetics.

[75]  Joachim M. Buhmann,et al.  Sensory segmentation with coupled neural oscillators , 1992, Biological Cybernetics.

[76]  J. Changeux,et al.  SYNAPTIC PLASTICITY AS BASIS OF BRAIN ORGANIZATION , 2022 .

[77]  D. Prince,et al.  Functional Properties of Neocortical Neurons , 2022 .