Resonating neurons stabilize heterogeneous grid-cell networks

  1. Divyansh Mittal
  2. Rishikesh Narayanan  Is a corresponding author
  1. Indian Institute of Science, Bangalore, India

Abstract

A central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. Here, we investigated the impact of distinct forms of biological heterogeneities on the stability of a two-dimensional continuous attractor network (CAN) implicated in grid-patterned activity generation. We show that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that targeted suppression of low-frequency perturbations could ameliorate heterogeneity-induced disruptions of grid-patterned activity. To test this, we introduced intrinsic resonance, a physiological mechanism to suppress low-frequency activity, either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We found CAN networks with mechanistic resonators to be more effective in targeted suppression of low-frequency activity, with the slow kinetics of the negative feedback loop essential in stabilizing these networks. As low-frequency perturbations (1/f noise) are pervasive across biological systems, our analyses suggest a universal role for mechanisms that suppress low-frequency activity in stabilizing heterogeneous biological networks.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source code employed for simulations are uploaded.

Article and author information

Author details

  1. Divyansh Mittal

    Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Bangalore, India
    Competing interests
    The authors declare that no competing interests exist.
  2. Rishikesh Narayanan

    Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Bangalore, India
    For correspondence
    rishi@iisc.ac.in
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1362-4635

Funding

The Wellcome Trust DBT India Alliance (Senior Fellowship IA/S/16/2/502727)

  • Rishikesh Narayanan

Human Frontier Science Program (Career development award)

  • Rishikesh Narayanan

Department of Biotechnology, Ministry of Science and Technology, India (DBT-IISc partnership Program)

  • Rishikesh Narayanan

Revati and Satya Nadham Atluri Chair (Chair Professorship)

  • Rishikesh Narayanan

Ministry of Human Resource Development (Scholarship funds)

  • Divyansh Mittal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Mittal & Narayanan

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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  1. Divyansh Mittal
  2. Rishikesh Narayanan
(2021)
Resonating neurons stabilize heterogeneous grid-cell networks
eLife 10:e66804.
https://doi.org/10.7554/eLife.66804

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https://doi.org/10.7554/eLife.66804

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