Abstract

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish's muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control of locomotion.

Data availability

No experimental data sets were generated in this study. Simulation parameters for all figures can be found in the manuscript and its supplements.Hydrodynamics simulations were performed with the IB2D package by Nicholas A. Battista (https://github.com/nickabattista/ib2d).

Article and author information

Author details

  1. Fabian Pallasdies

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    For correspondence
    fabianpallasdies@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5359-4699
  2. Sven Goedeke

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Wilhelm Braun

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9419-3311
  4. Raoul Memmesheimer

    Neural Network Dynamics and Computation, Institute of Genetics, University of Bonn, Bonn, Germany
    For correspondence
    rm.memmesheimer@uni-bonn.de
    Competing interests
    The authors declare that no competing interests exist.

Funding

The German Federal Ministry of Education and Research (01GQ1710)

  • Fabian Pallasdies
  • Sven Goedeke
  • Wilhelm Braun
  • Raoul Memmesheimer

SMARTSTART Joint Training Program of the Bernstein Network and the VolkswagenStiftung (SmartStart2)

  • Fabian Pallasdies

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

Reviewing Editor

  1. Ronald L Calabrese, Emory University, United States

Version history

  1. Received: July 10, 2019
  2. Accepted: December 22, 2019
  3. Accepted Manuscript published: December 23, 2019 (version 1)
  4. Version of Record published: February 3, 2020 (version 2)
  5. Version of Record updated: May 27, 2020 (version 3)

Copyright

© 2019, Pallasdies et al.

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. Fabian Pallasdies
  2. Sven Goedeke
  3. Wilhelm Braun
  4. Raoul Memmesheimer
(2019)
From single neurons to behavior in the jellyfish Aurelia aurita
eLife 8:e50084.
https://doi.org/10.7554/eLife.50084

Share this article

https://doi.org/10.7554/eLife.50084

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