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Incorporating buccal mass planar mechanics and anatomical features improves neuromechanical modeling of Aplysia feeding behavior

  • Michael J. Bennington
  • , Ashlee S. Liao
  • , Ravesh Sukhnandan
  • , Bidisha Kundu
  • , Stephen M. Rogers
  • , Jeffrey P. Gill
  • , Jeffrey M. McManus
  • , Gregory P. Sutton
  • , Hillel J. Chiel
  • , Victoria A. Webster-Wood*
  • *Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)

Abstract

To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine mollusk Aplysia californica. Previous in silico and in roboto models have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to match in vivo data both qualitatively and quantitatively. We introduce a new neuromechanical model of Aplysia feeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics of Aplysia feeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulated feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen in Aplysia and demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass.
Original languageEnglish
Article number17
Number of pages29
JournalBiological Cybernetics
Volume119
Issue number4
Early online date7 Jul 2025
DOIs
Publication statusE-pub ahead of print - 7 Jul 2025

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 14 - Life Below Water
    SDG 14 Life Below Water

Keywords

  • Computational model
  • Neuromechanics
  • Aplysia californica
  • Quasistatic mechanics

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