Dynamic link models for global decision making with binding-by-synchrony

We address the problem of integrating information about multiple objects and their positions on a visual scene. A primate visual system has fewer difficulties in rapidly achieving integration, given even when presented with several objects. Here, we propose a neurally plausible mechanism for simultaneously coordinating the local decision-making process of “what”- and “where”-information for the organization of global multi-object recognition. The mechanism is based on paradigms of binding-by-synchrony and dynamic link matching in a network system of the macrocolumnar cortical model. These paradigms are responsible for encoding an individual object and its position through a synchronization-desynchronization process among selected or unselected links of the objects.

[1]  Elie Bienenstock,et al.  A neural network for the retrieval of superimposed connection patterns , 1987 .

[2]  C Koch,et al.  Slow synaptic transmission in frog sympathetic ganglia. , 1986, The Journal of experimental biology.

[3]  Makoto Miyake,et al.  A nonlinear oscillator network circuit for image segmentation with double-threshold phase detection , 1999 .

[4]  Thomas Burwick.  Oscillatory Neural Networks with Self-Organized Segmentation of Overlapping Patterns , 2007, Neural Computation.

[5]  G. Buzsáki,et al.  Gamma Oscillation by Synaptic Inhibition in a Hippocampal Interneuronal Network Model , 1996, The Journal of Neuroscience.

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

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

[8]  Philipp Wolfrum,et al.  Information Routing, Correspondence Finding, and Object Recognition in the Brain , 2010, Studies in Computational Intelligence.

[9]  C. Gray,et al.  Chattering Cells: Superficial Pyramidal Neurons Contributing to the Generation of Synchronous Oscillations in the Visual Cortex , 1996, Science.

[10]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[11]  Tim Kiemel,et al.  Relative Phase Behavior of Two Slowly Coupled Oscillators , 1993, SIAM J. Appl. Math..

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

[13]  W. Singer,et al.  Stimulus‐Dependent Neuronal Oscillations in Cat Visual Cortex: Receptive Field Properties and Feature Dependence , 1990, The European journal of neuroscience.

[14]  Yasuomi D. Sato,et al.  Generalization of coupled spiking models and effects of the width of an action potential on synchronization phenomena. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  H. Wilson.  Spikes, Decisions, and Actions: The Dynamical Foundations of Neuroscience , 1999 .

[16]  Tomoki Fukai,et al.  Synchronous and asynchronous bursting states: role of intrinsic neural dynamics , 2007, Journal of Computational Neuroscience.

[17]  Christoph von der Malsburg,et al.  Experience-Driven Formation of Parts-Based Representations in a Model of Layered Visual Memory , 2009, Front. Comput. Neurosci..

[18]  Christoph von der Malsburg,et al.  Recognizing Faces by Dynamic Link Matching , 1996, NeuroImage.

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

[20]  T. Matsuzawa,et al.  Working memory of numerals in chimpanzees , 2007, Current Biology.