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. Computational models that embed artificial neural controllers within body models in simulated environments, are a powerful tool for this purpose. Here, we review advances in biorealistic neuromechanical models 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 models 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 surrogates 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，要理解行为控制的算法，必须考虑生物力学和环境背景。将人工神经控制器嵌入身体模型并在模拟环境中运行的计算模型，是实现这一目标的强大工具。本文综述了生物逼真神经机械模型的最新进展，同时展望了即将涌现的新机遇。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。通过系统性扰动，可借助这些模型生成可实验检验的新假设。随后，我们探讨了神经机械模型如何促进神经科学、机器人学和机器学习之间的交流，并展示了其在医疗保健领域的应用。我们展望，将实验研究与对神经机械代理的主动探测相结合，将显著加速神经科学的发展。