A hierarchical generative model of recurrent object-based attention in the visual cortex

  • Authors:

  • David P. Reichert;Peggy Series;Amos J. Storkey

  • Affiliations:

  • School of Informatics, University of Edinburgh, Edinburgh, UK;School of Informatics, University of Edinburgh, Edinburgh, UK;School of Informatics, University of Edinburgh, Edinburgh, UK

  • Venue:
  • ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
  • Year:
  • 2011

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Abstract

In line with recent work exploring Deep Boltzmann Machines (DBMs) as models of cortical processing, we demonstrate the potential of DBMs as models of object-based attention, combining generative principles with attentional ones. We show: (1) How inference in DBMs can be related qualitatively to theories of attentional recurrent processing in the visual cortex; (2) that deepness and topographic receptive fields are important for realizing the attentional state; (3) how more explicit attentional suppressive mechanisms can be implemented, depending crucially on sparse representations being formed during learning.