Abstract
Animal behavior reflects interactions between the nervous system, body, and environment. Therefore, biomechanics and environmental context must be considered to dissect algorithms for behavioral control. This is enabled by leveraging neuromechanical digital twins: computational models that embed artificial neural controllers within realistic body models in simulated environments. Here we review advances in the creation and use of neuromechanical digital twins while also highlighting emerging opportunities for the future. First, we illustrate how neuromechanical models allow researchers to infer hidden biophysical variables that may be difficult to measure experimentally. Additionally, by perturbing these models, one can generate new experimentally testable hypotheses. Next, we explore how neuromechanical twins have been used to foster a deeper exchange between neuroscience, robotics, and machine learning. Finally, we show how neuromechanical twins can advance healthcare. We envision that coupling studies on animals with active probing of their neuromechanical twins will greatly accelerate neuroscientific discovery.
Abstract (translated)
动物行为反映了神经系统、身体和环境之间的相互作用。因此，为了剖析控制行为的算法，必须考虑生物力学和环境背景。这可以通过利用神经机械数字孪生来实现：将人工神经控制器嵌入到模拟环境中真实的机体模型中的计算模型。在这里，我们回顾了在创建和使用神经机械数字孪生方面的进展，并同时强调未来出现的新机遇。 首先，我们将展示神经机械模型如何使研究人员能够推断出可能难以通过实验测量的隐含生物物理变量。此外，通过扰动这些模型，可以生成新的可实验验证假设。其次，我们探讨了神经机械孪生如何被用于促进神经科学、机器人技术与机器学习之间的更深层次交流。最后，我们将展示神经机械孪生如何推进医疗保健领域的发展。 我们设想将对动物的研究与其神经机械孪生的主动探索相结合，这将极大地加速神经科学研究的进展。
URL
https://arxiv.org/abs/2601.08056
