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Analysis Pipeline for High-Dimensional Neuromechanical Model Improvement

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Biomimetic and Biohybrid Systems(Living Machines 2024)

Abstract

To capture and understand animal behavior, engineers and biologists seek to develop biologically accurate neuromechanical models of muscle dynamics and neural control. However, demand-driven enhancement of complex neuromechanics, such as the multifunctionalAplysia californica feeding apparatus, can be challenging due to the multidimensional biomechanical and neural models involved. We propose an analysis pipeline that enables reinforcement learning (RL) to classify which aspects of an engineered neuromechanical model can accurately capture animal behavior. As an example, prioritizing where demand-driven enhancement of a biomechanical and neural model is needed, the neural model of a published neuromechanical model ofAplysia swallowing during feeding was replaced with an RL controller and their performances were compared and correlated within vivo swallowing behavior. By comparing the performance of the neural model and the learned model toin vivo animal behavior, we can pinpoint areas for improvement. The analysis pipeline identified that the neuromechanical model confidently captured force performance with no significant difference from animal swallowing force behavior. It most usefully also indicated that the biomechanical model will need to be improved in future iterations to better capture motor neuron activity. Future work should explore the accuracy of the RL-enabled analysis pipeline with a more advanced biomechanical model.

This work was supported by NSF DBI2015317 as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program, by the NSF Research Fellowship Program under Grant No. DGE1745016, and by internal funding through Carnegie Mellon University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Acknowledgements

The authors thank Dr. Jeff Gill for sharing the animal data from his 2020 publication [9] for use in these comparisons. We thank the anonymous reviewers for their insightful edits and suggestions.

Author information

Authors and Affiliations

  1. Department of Mechanical, Carnegie Mellon University, Pittsburgh, PA, 15232, USA

    Camila J. Fernandez, Michael J. Bennington & Victoria A. Webster-Wood

  2. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, 15232, USA

    Victoria A. Webster-Wood

  3. McGowan Institute for Regenerative Medicine, Carnegie Mellon University, Pittsburgh, PA, 15232, USA

    Victoria A. Webster-Wood

  4. Department of Biology, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA

    Jeffrey M. McManus & Hillel J. Chiel

  5. Mechanical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA

    Yanjun Li & Roger D. Quinn

  6. Neurosciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA

    Hillel J. Chiel

  7. Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA

    Hillel J. Chiel

Authors
  1. Camila J. Fernandez
  2. Jeffrey M. McManus
  3. Yanjun Li
  4. Michael J. Bennington
  5. Roger D. Quinn
  6. Hillel J. Chiel
  7. Victoria A. Webster-Wood

Corresponding author

Correspondence toCamila J. Fernandez.

Editor information

Editors and Affiliations

  1. West Virginia University, Morgantown, WV, USA

    Nicholas S. Szczecinski

  2. Carnegie Mellon University, Pittsburgh, USA

    Victoria Webster-Wood

  3. Northwestern University, Evanston, IL, USA

    Matthew Tresch

  4. Case Western Reserve University, Cleveland, OH, USA

    William R. P. Nourse

  5. Radboud University, Nijmegen, Gelderland, The Netherlands

    Anna Mura

  6. Case Western Reserve University, Cleveland, OH, USA

    Roger D. Quinn

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© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Fernandez, C.J.et al. (2025). Analysis Pipeline for High-Dimensional Neuromechanical Model Improvement. In: Szczecinski, N.S., Webster-Wood, V., Tresch, M., Nourse, W.R.P., Mura, A., Quinn, R.D. (eds) Biomimetic and Biohybrid Systems. Living Machines 2024. Lecture Notes in Computer Science(), vol 14930. Springer, Cham. https://doi.org/10.1007/978-3-031-72597-5_23

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