Synchronized oscillations during cooperative feature linking in a cortical model of visual perception

Abstract A neural network model of synchronized oscillator activity in visual cortex is presented in order to account for recent neurophysiological findings that such synchronization may reflect global properties of the stimulus. In these recent experiments, it was reported that synchronization of oscillatory firing responses to moving bar stimuli occurred not only for nearby neurons, but also occurred between neurons separated by several cortical columns (several mm of cortex) when these neurons shared some receptive field preferences specific to the stimuli. These results were obtained not only for single bar stimuli but also across two disconnected, but colinear, bars moving in the same direction. Our model and computer simulations obtain these synchrony results across both single and double bar stimuli. For the double bar case, synchronous oscillations are induced in the region between the bars, but no oscillations are induced in the regions beyond the stimuli. These results were achieved with cellular units that exhibit limit cycle oscillations for a robust range of input values, but which approach an equilibrium state when undriven. Single and double bar synchronization of these oscillators was achieved by different, but formally related, models of preattentive visual boundary segmentation and attentive visual object recognition, as well as nearest-neighbor and randomly coupled models. In preattentive visual segmentation, synchronous oscillations may reflect the binding of local feature detectors into a globally coherent grouping. In object recognition, synchronous oscillations may occur during an attentive resonant state that triggers new learning. These modelling results support earlier theoretical predictions of synchronous visual cortical oscillations and demonstrate the robustness of the mechanisms capable of generating synchrony.

[1]  S. Grossberg.  Biological competition: Decision rules, pattern formation, and oscillations. , 1980, Proceedings of the National Academy of Sciences of the United States of America.

[2]  W. Rall,et al.  Experimental monosynaptic input-output relations in the mammalian spinal cord. , 1955, Journal of cellular and comparative physiology.

[3]  Stephen Grossberg,et al.  ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.

[4]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

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

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

[7]  S. Grossberg.  Contour Enhancement , Short Term Memory , and Constancies in Reverberating Neural Networks , 1973 .

[8]  Wilfrid Rall,et al.  ANALYSIS OF REFLEX VARIABILITY IN TERMS OF PARTIALLY CORRELATED EXCITABILITY FLUCTUATION IN A POPULATION OF MOTONEURONS , 1956, The Journal of general physiology.

[9]  S. Grossberg.  Competition, Decision, and Consensus , 1978 .

[10]  GrossbergS..  Adaptive pattern classification and universal recoding , 1976 .

[11]  Amir F. Atiya,et al.  Oscillations and Synchronizations in Neural Networks: an Exploration of the Labeling Hypothesis , 1991, Int. J. Neural Syst..

[12]  S. Grossberg.  Outline of A Theory of Brightness, Color, and form Perception , 1984 .

[13]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[14]  S Grossberg,et al.  Cortical dynamics of three-dimensional form, color, and brightness perception: II. Binocular theory , 1988, Perception & psychophysics.

[15]  S. Grossberg.  Decisions, patterns, and oscillations in nonlinear competitive systems with applications to Volterra-Lotka systems. , 1978, Journal of theoretical biology.

[16]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[17]  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.

[18]  G. Ermentrout,et al.  Oscillator death in systems of coupled neural oscillators , 1990 .

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

[20]  W. Rall.  A statistical theory of monosynaptic input-output relations. , 1955, Journal of cellular and comparative physiology.

[21]  Pierre Baldi,et al.  Computing with Arrays of Coupled Oscillators: An Application to Preattentive Texture Discrimination , 1990, Neural Computation.

[22]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[23]  S. Grossberg.  Associative and Competitive Principles of Learning and Development: The Temporal Unfolding and Stability of Stm and Ltm Patterns , 1987 .

[24]  Ennio Mingolla,et al.  Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations , 1985 .

[25]  M M Sondhi,et al.  Model for visual luminance discrimination and flicker detection. , 1968, Journal of the Optical Society of America.

[26]  Stephen Grossberg,et al.  Neural dynamics of surface perception: Boundary webs, illuminants, and shape-from-shading , 1987, Comput. Vis. Graph. Image Process..

[27]  Stephen Grossberg,et al.  Stereo boundary fusion by cortical complex cells: A system of maps, filters, and feedback networks for multiplexing distributed data , 1989, Neural Networks.

[28]  S. Grossberg.  Studies of mind and brain : neural principles of learning, perception, development, cognition, and motor control , 1982 .

[30]  T. Wiesel,et al.  Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[31]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[32]  Stephen Grossberg,et al.  Nonlinear neural networks: Principles, mechanisms, and architectures , 1988, Neural Networks.

[33]  S Grossberg,et al.  Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance , 1984, Perception & Psychophysics.

[34]  S. Grossberg.  Cortical dynamics of three-dimensional form, color, and brightness perception: I. Monocular theory , 1987, Perception & psychophysics.