HUXLEY SURROGATE MODEL FOR TWITCH MUSCLE CONTRACTION

B. Milićević, M. Ivanovic, B. Stojanovic, N. Filipovic
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Abstract

Biophysical muscle models, often called Huxley-type models, are based on the underlying physiology of muscles, making them suitable for modeling non-uniform and unsteady contractions. This kind of model can be computationally intensive, which makes the usage of large-scale simulations difficult. To enable more efficient usage of the Huxley muscle model, we created a data-driven surrogate model, which behaves similarly to the original Huxley muscle model, but it requires significantly less computational power. From several numerical simulations, we acquired a lot of data and trained deep neural networks so that the behavior of the neural network resembles the behavior of the Huxley model. Since muscle models are history-dependent we used time series as an input and we trained a recurrent neural network to produce stress and instantaneous stiffness. The real challenge was to get the neural network to predict these values precisely enough for the numerical simulation to work properly and produce accurate results. In our work, we showed results obtained with the original Huxley model and surrogate Huxley model for several muscle twitch contractions. Based on similarities between the surrogate model and the original model we can conclude that the surrogate has the potential to replace the original model within numerical simulations.
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抽搐肌收缩的赫胥黎替代模型
生物物理肌肉模型,通常被称为赫胥黎模型,是基于肌肉的潜在生理学,使其适合于模拟非均匀和非定常收缩。这种模型的计算量非常大,这使得大规模模拟的使用变得困难。为了更有效地使用赫胥黎肌模型,我们创建了一个数据驱动的代理模型,其行为类似于原始的赫胥黎肌模型,但它需要的计算能力显着降低。从几个数值模拟中,我们获得了大量的数据,并训练了深度神经网络,使神经网络的行为类似于赫胥黎模型的行为。由于肌肉模型是历史相关的,我们使用时间序列作为输入,我们训练了一个循环神经网络来产生应力和瞬时刚度。真正的挑战是让神经网络足够精确地预测这些值,以使数值模拟正常工作并产生准确的结果。在我们的工作中,我们展示了原始赫胥黎模型和替代赫胥黎模型对几种肌肉抽搐收缩的结果。基于代理模型与原始模型之间的相似性,我们可以得出结论,代理模型在数值模拟中具有取代原始模型的潜力。
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