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.

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，必须结合生物力学与环境背景来解析行为控制的算法机制。神经力学数字孪生为此提供了可能：这类计算模型将人工神经控制器嵌入模拟环境中的逼真身体模型内。本文综述了神经力学数字孪生的构建与应用进展，并展望其未来发展的新机遇。首先，我们阐述神经力学模型如何帮助研究者推断实验中难以测量的隐式生物物理变量。此外，通过对这些模型进行扰动分析，可以生成新的可实验验证的假说。接着，我们探讨神经力学数字孪生如何促进神经科学、机器人学与机器学习之间的深度交叉融合。最后，我们展示神经力学数字孪生在医疗健康领域的应用前景。我们预见，将动物研究与对其神经力学数字孪生的主动探测相结合，将极大推动神经科学研究的进展。