NDLI: Sparse coding with a global connectivity constraint

Content Provider	IEEE Xplore Digital Library
Author	Thomas, R.M. Yatawatta, S. Keysers, C.
Copyright Year	2014
Description	Author affiliation: The Netherlands Institute for Radio Astronomy, Dwingeloo, The Netherlands (Yatawatta, S.) \|\| Netherlands Institute for Neuroscience, Amsterdam, The Netherlands (Thomas, R.M.; Keysers, C.)
Abstract	Basis pursuit via sparse coding techniques have generally enforced sparseness by using L1-type norms on the coefficients of the bases. When applied to natural scenes these algorithms famously retrieve the Gabor-like basis functions of the primary visual cortex (V1) of the mammalian brain. In this paper, inspired further by the architecture of the brain, we propose a technique that not only retrieves the Gabor basis but does so respecting global power-law type connectivity patterns. Such global constraints are beneficial from a biological perspective in terms of efficient wiring, robustness etc. We draw on the similarity between sparse coding and neural networks to formulate the problem and impose such global connectivity patterns.
Starting Page	4087
Ending Page	4091
File Size	329542
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Image reconstruction Presses Wiring Brain modeling Cost function biologically inspired sparse coding scale-free networks
Content Type	Text
Resource Type	Article

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