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. © 2011 Springer-Verlag.
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CITATION STYLE
Reichert, D. P., Series, P., & Storkey, A. J. (2011). A hierarchical generative model of recurrent object-based attention in the visual cortex. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6791 LNCS, pp. 18–25). Springer Verlag. https://doi.org/10.1007/978-3-642-21735-7_3
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