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Visual Information Processing Mechanism Revealed by fMRI Data

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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Abstract

The functional Magnetic Resonance Imaging (fMRI) data of both the ventral pathway and the dorsal pathway on the visual cortex in a classification task was analyzed. We found that the classification performance improved hierarchically from lower-level regions to higher-level regions in both pathways, which partly verified the visual pathway theory proposed in cognitive neuroscience. Moreover, the LO (Lateral Occipital), V3a and V3b fMRI data were good classification basis no worse than the widely-used features such as GIST, HOG and LBP. It indicated that imitating the activity patterns of visual cortex to design new feature-extraction algorithms might be favorable. Finally, the performance of V3a and V3b voxels were very close to that of LO voxels. Consequently, in the design of brain-like intelligence systems, we should consider the coordination mechanism between the two pathways rather than focusing on the ventral pathway alone. The relationship of human visual pathway and deep learning structure was also discussed tersely.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61271151, 91520202) and Youth Innovation Promotion Association, Chinese Academy of Sciences (CAS).

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Authors and Affiliations

  1. Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jinpeng Li, Zhaoxiang Zhang & Huiguang He

  2. University of Chinese Academy of Sciences (UCAS), Beijing, China

    Jinpeng Li, Zhaoxiang Zhang & Huiguang He

  3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang & Huiguang He

Authors
  1. Jinpeng Li
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  2. Zhaoxiang Zhang
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  3. Huiguang He
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Corresponding author

Correspondence to Huiguang He .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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Li, J., Zhang, Z., He, H. (2016). Visual Information Processing Mechanism Revealed by fMRI Data. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

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