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Activity—Gating Attentional Networks

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Book cover Models of Neural Networks IV

Part of the book series: Physics of Neural Networks ((NEURAL NETWORKS))

Abstract

In the visual system, “attention” selectively enhances and expedites the processing of a subset of the available stimuli vs. the rest. Attention can be directed to many different feature dimensions, such as location, form, color, texture and direction of movement. In this work, we present a model of attentional processing that makes extensive use of the feedforward, lateral and feedback connections known to exist in the visual cortex. The model uses local modulations of the activity of neuronal ensembles to superpose additional saliency and attentional information on top of the sensory data. The additional signals “gate” the information through the entire network and trigger response competition, resulting in an attentional concentration of the processing resources. At the network level, the model consists of two complementary information counterstreams that process separately sensory and attentional data: A sensory, feedforward stream directly analyses the features available in the stimulus, while an attentional stream provides expectations and global hypotheses about the stimulus. We explain the function of such a network as a hypothesis generating and confirming system. We also explain the architecture, components and dynamics necessary for the implementation of such an activity-gating network. The goal is to arrive at a consistent and unified model of attentional processing in the visual system that explains the different types of attention within a single framework.

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Authors
  1. J. Eggert
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  2. J. L. van Hemmen
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Editor information

Editors and Affiliations

  1. Institut für Theoretische Physik, Technische Universität München, D-85747, Garching bei München, Germany

    J. Leo van Hemmen

  2. Department of Mathematics, University of Chicago, 60637, Chicago, IL, USA

    Jack D. Cowan

  3. Department of Electronics, Weizmann Institute of Science, 76100, Rehovot, Israel

    Eytan Domany

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Eggert, J., van Hemmen, J.L. (2002). Activity—Gating Attentional Networks. In: van Hemmen, J.L., Cowan, J.D., Domany, E. (eds) Models of Neural Networks IV. Physics of Neural Networks. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21703-1_7

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  • DOI: https://doi.org/10.1007/978-0-387-21703-1_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-2875-7

  • Online ISBN: 978-0-387-21703-1

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