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Sparse Representation via Intracellular and Extracellular Mechanisms

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Advances in Neural Networks – ISNN 2015 (ISNN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

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Abstract

Sparse representation in sensory cortex has been well verified and its capability of yielding response properties of single neurons is also demonstrated. In order to improve sparse representation to be more neurally plausible, we reconsider several response properties of single neurons, especially the cross orientation suppression and surround suppression. A new sparse representation model using intracellular and extracellular neural mechanisms is presented. Simulation results of the presented model explain physiological observations very well.

This work was supported by the Science Research Foundation for High-level Talents of Guizhou Institute of Technology XJGC20130902, and in part by the Science and Technology Foundation of Guizhou Province J[2014]2081 and by the Innovation Team of Guizhou Provincial Eduction Department under Grant No [2014] 34.

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Authors and Affiliations

  1. School of Information Engineering, Guizhou Institute of Technology, Guiyang, Guizhou Province, 550003, P.R. China

    Jiqian Liu & Chengbin Zeng

Authors
  1. Jiqian Liu
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  2. Chengbin Zeng
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Corresponding author

Correspondence to Jiqian Liu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Liu, J., Zeng, C. (2015). Sparse Representation via Intracellular and Extracellular Mechanisms. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_50

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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