Animal behavior reflects interactions between the nervous system, body, and environment. Therefore, biomechanics and environmental context must be considered to understand algorithms for behavioral control. Neuromechanical digital twins, namely computational models that embed artificial neural controllers within realistic body models in simulated environments, are a powerful tool for this purpose. Here, we review advances in neuromechanical digital twins while also highlighting emerging opportunities ahead. We first show how these models enable inference of biophysical variables that are difficult to measure experimentally. Through systematic perturbation, one can generate new experimentally testable hypotheses through these models. We then examine how neuromechanical twins facilitate the exchange between neuroscience, robotics, and machine learning, and showcase their applications in healthcare. We envision that coupling experimental studies with active probing of their neuromechanical twins will significantly accelerate progress in neuroscience.

翻译：动物行为反映了神经系统、身体与环境之间的相互作用。因此，理解行为控制算法必须考虑生物力学与环境背景。神经力学数字孪生——即在模拟环境中将人工神经控制器嵌入逼真身体模型的计算模型——是实现这一目标的有力工具。本文综述了神经力学数字孪生的研究进展，同时展望了未来的发展机遇。我们首先展示了这些模型如何推断实验难以测量的生物物理变量。通过系统扰动，这些模型可以生成可实验验证的新假说。随后，我们探讨了神经力学孪生如何促进神经科学、机器人学和机器学习之间的交流，并展示了其在医疗健康领域的应用。我们预期，将实验研究与神经力学孪生体的主动探测相结合，将显著加速神经科学的进步。