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A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space

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  • Karthik Soman

    (Indian Institute of Technology Madras
    University of California–Berkeley)

  • Srinivasa Chakravarthy

    (Indian Institute of Technology Madras)

  • Michael M. Yartsev

    (University of California–Berkeley)

Abstract

Three-dimensional (3D) spatial cells in the mammalian hippocampal formation are believed to support the existence of 3D cognitive maps. Modeling studies are crucial to comprehend the neural principles governing the formation of these maps, yet to date very few have addressed this topic in 3D space. Here we present a hierarchical network model for the formation of 3D spatial cells using anti-Hebbian network. Built on empirical data, the model accounts for the natural emergence of 3D place, border, and grid cells, as well as a new type of previously undescribed spatial cell type which we call plane cells. It further explains the plausible reason behind the place and grid-cell anisotropic coding that has been observed in rodents and the potential discrepancy with the predicted periodic coding during 3D volumetric navigation. Lastly, it provides evidence for the importance of unsupervised learning rules in guiding the formation of higher-dimensional cognitive maps.

Suggested Citation

  • Karthik Soman & Srinivasa Chakravarthy & Michael M. Yartsev, 2018. "A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06441-5
    DOI: 10.1038/s41467-018-06441-5
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    Cited by:

    1. Jiaojiao Xu & Chuanjie Yan & Yangyang Su & Yong Liu, 2020. "Analysis of high-rise building safety detection methods based on big data and artificial intelligence," International Journal of Distributed Sensor Networks, , vol. 16(6), pages 15501477209, June.

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