A feature-binding model with localized excitations

Authors:
Hecke Schrobsdorff;J. Michael Herrmann;Theo Geisel
Affiliations:
Bernstein Center for Computational Neuroscience Göttingen, Germany and Georg-August University Göttingen, Institute for Nonlinear Dynamics, Germany;Bernstein Center for Computational Neuroscience Göttingen, Germany and Georg-August University Göttingen, Institute for Nonlinear Dynamics, Germany;Bernstein Center for Computational Neuroscience Göttingen, Germany and Georg-August University Göttingen, Institute for Nonlinear Dynamics, Germany and Max-Planck Institute for Dynamics ...
Venue:
Neurocomputing
Year:
2007

Citing 4
Cited 1

1994 Special Issue: A fast dynamic link matching algorithm for invariant pattern recognition

Neural Networks - Special issue: models of neurodynamics and behavior
Breathers in nonlinear lattices: existence, linear stability and quantization

Proceedings of the workshop on Lattice dynamics
Synchronization of neuronal responses as a putative binding mechanism

The handbook of brain theory and neural networks
A Computational Model of Prefrontal Control in Free Recall: Strategic Memory Use in the California Verbal Learning Task

Journal of Cognitive Neuroscience

A computational approach to negative priming

Connection Science

Quantified Score

Hi-index	0.04

Visualization

Abstract

We study a model of feature binding in prefrontal cortex which defers specific perceptual information to lower areas and merely maintains the identity of the combination. The model consists of three layers of pulse-coupled leaky integrate-and-fire neurons. Features are encoded by the location of sustained activity in the subordinate layers. The feature layers are excitatorily coupled to a superordinate layer that represents combinations of features by means of an oscillatory dynamics. The model accounts for effects such as the memorization of an object that was perceived only for a short period, illusory binding of simultaneous stimuli, and the limit of attentional capacity. The present paper discusses conditions for localized excitations in networks of integrate-and-fire neurons and considers the application to a dynamic link architecture.