An action potential-driven model of soleus muscle activation dynamics for locomotor-like movements

Hojeong Kim, Thomas G. Sandercock, C. J. Heckman

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Objective. The goal of this study was to develop a physiologically plausible, computationally robust model for muscle activation dynamics (A(t)) under physiologically relevant excitation and movement. Approach. The interaction of excitation and movement on A(t) was investigated comparing the force production between a cat soleus muscle and its Hill-type model. For capturing A(t) under excitation and movement variation, a modular modeling framework was proposed comprising of three compartments: (1) spikes-to-[Ca2+]; (2) [Ca2+]-to-A; and (3) A-to-force transformation. The individual signal transformations were modeled based on physiological factors so that the parameter values could be separately determined for individual modules directly based on experimental data. Main results. The strong dependency of A(t) on excitation frequency and muscle length was found during both isometric and dynamically-moving contractions. The identified dependencies of A(t) under the static and dynamic conditions could be incorporated in the modular modeling framework by modulating the model parameters as a function of movement input. The new modeling approach was also applicable to cat soleus muscles producing waveforms independent of those used to set the model parameters. Significance. This study provides a modeling framework for spike-driven muscle responses during movement, that is suitable not only for insights into molecular mechanisms underlying muscle behaviors but also for large scale simulations.

Original languageEnglish (US)
Article number046025
JournalJournal of Neural Engineering
Issue number4
StatePublished - Aug 1 2015


  • activation dynamics
  • cat soleus
  • computer simulation
  • muscle modeling
  • muscle movement
  • spike excitation

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Biomedical Engineering


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