4.6 Article

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

期刊

JOURNAL OF NEURAL ENGINEERING
卷 12, 期 4, 页码 -

出版社

IOP PUBLISHING LTD
DOI: 10.1088/1741-2560/12/4/046025

关键词

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

资金

  1. National Institutes of Health Grant [R01 NS071951, NS062200]
  2. DGIST R&D Program of the Ministry of Science, ICT and Future Planning of Korea [13-01-HRSS-03, 14-RS-02]
  3. NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS071951, R01NS062200] Funding Source: NIH RePORTER

向作者/读者索取更多资源

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.

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