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Segmentation, Binding, and Illusory Conjunctions.

D Horn1, D Sagi2, M Usher2

  • 1School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel.

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|June 7, 2019
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This study models neural networks to explain how the brain binds object attributes like shape and color. The model successfully binds attributes for two objects but creates illusory conjunctions for more than two, mimicking human vision limitations.

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Area of Science:

  • Computational Neuroscience
  • Cognitive Neuroscience
  • Neural Network Modeling

Background:

  • The brain's ability to bind different attributes of an object (e.g., shape, color) into a unified perception is a fundamental cognitive process.
  • Neural network models offer a powerful framework for investigating the mechanisms underlying complex cognitive functions like binding.
  • Oscillating neural networks, composed of excitatory and inhibitory cell assemblies, provide a biologically plausible substrate for information processing.

Purpose of the Study:

  • To investigate the neural mechanisms of attribute binding using a computational model of oscillating neural networks.
  • To determine if a model of interconnected excitatory and inhibitory cell assemblies can replicate the phenomenon of attribute binding.
  • To explore the limitations of such a model when processing multiple object attributes, specifically examining the emergence of illusory conjunctions.

Main Methods:

  • Developed a computational model comprising two interconnected oscillating neural networks.
  • Excitatory cell assemblies within each network represented object attributes (e.g., shape, color).
  • Simulated the segmentation of input data containing mixed attribute pairs and observed the phase locking of oscillating activities to demonstrate binding.

Main Results:

  • The model successfully demonstrated attribute binding for pairs of objects, indicated by the phase locking of relevant neural activities.
  • When presented with more than two objects, the model exhibited faulty correlations, conjoining attributes from different objects.
  • These faulty correlations mirror the phenomenon of illusory conjunctions observed in human visual perception.

Conclusions:

  • Oscillating neural networks with interconnected excitatory and inhibitory assemblies can model the binding of object attributes.
  • The model's limitations in handling multiple objects highlight potential neural mechanisms contributing to illusory conjunctions in vision.
  • This research provides insights into the computational principles underlying perceptual binding and its potential failure modes.