A geometric attractor mechanism for self-organization of entorhinal grid modules
Abstract
Grid cells in the medial entorhinal cortex (MEC) respond when an animal occupies a periodic lattice of 'grid fields' in the environment. The grids are organized in modules with spatial periods, or scales, clustered around discrete values separated on average by ratios in the range 1.4-1.7. We propose a mechanism that produces this modular structure through dynamical self-organization in the MEC. In attractor network models of grid formation, the grid scale of a single module is set by the distance of recurrent inhibition between neurons. We show that the MEC forms a hierarchy of discrete modules if a smooth increase in inhibition distance along its dorso-ventral axis is accompanied by excitatory interactions along this axis. Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids and have values that fall within the observed range. We discuss how interactions required by our model might be tested experimentally.
Data availability
We have included the source code for our main simulation as a supporting file.
Article and author information
Author details
Funding
Honda Research Institute
- Vijay Balasubramanian
National Science Foundation (PHY-1734030)
- Vijay Balasubramanian
Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley (Postdoctoral fellowship)
- Louis Kang
National Institutes of Health (Medical Scientist Training Program)
- Louis Kang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Upinder Singh Bhalla, Tata Institute of Fundamental Research, India
Version history
- Received: March 8, 2019
- Accepted: August 1, 2019
- Accepted Manuscript published: August 2, 2019 (version 1)
- Version of Record published: October 3, 2019 (version 2)
Copyright
© 2019, Kang & Balasubramanian
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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