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A neural network model based on the cortical modularity

  • Original Paper - Cross-Disciplinary Physics and Related Areas of Science and Technology
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Abstract

Although the individual cortical areas in the brain support very diverse functions, their anatomical organizations are very similar, given by collections of cortical blocks with nearly the same structure. This character suggests that statistical dynamics in the brain may be modeled much efficiently if interactions between the cortical blocks and learning in them are described through effective reduction. This paper introduces a neural network model based on the cortical modularity. Specifically, in building the model, we postulate on how cortical blocks function and interact with each other and adopt the formalism of path-integral-based interactions and free-energy-based learning. We apply the model to explain the characteristics observed in early visual systems.

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Acknowledgements

This work was supported by the Sungshin Women’s University Research Grant of 2019-1-82-016/1. M.Y.C. also acknowledges the support from the National Research Foundation of Korea through the Basic Science Research Program (Grant No. 2019R1F1A1046285).

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Authors and Affiliations

  1. School of Biopharmaceutical and Medical Sciences, Sungshin Women’s University, Seoul, 01133, South Korea

    Myoung Won Cho

  2. Department of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, Seoul, 08826, South Korea

    M. Y. Choi

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  1. Myoung Won Cho
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  2. M. Y. Choi
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Correspondence to Myoung Won Cho or M. Y. Choi.

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Cho, M.W., Choi, M.Y. A neural network model based on the cortical modularity. J. Korean Phys. Soc. 79, 772–784 (2021). https://doi.org/10.1007/s40042-021-00301-0

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  • DOI: https://doi.org/10.1007/s40042-021-00301-0

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