An Empirically Driven Hierarchal Anti-Hebbian Network Model for the Formation of Spatial Cells in Three-Dimensional Space

Karthik Soman, Srinivasa Chakravarthy & Michael Yartsev

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Received: 3rd March 18

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.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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