HUXLEY SURROGATE MODEL FOR TWITCH MUSCLE CONTRACTION

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.