发表机构
RIKEN; Waseda University（理化学研究所; 早稻田大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建整合多模态解剖重建、生物力学模拟等的小鼠神经肌肉骨骼数字孪生模型，形成闭合感觉运动环路，有望推动生物智能研究及新型自适应生物医学、机器人系统开发。
AI 中文摘要
数字孪生技术可通过构建生物的预测性计算模型，变革神经科学与生物医学领域。然而，多数动物数字孪生模型仅分别对神经回路、解剖结构或生物力学进行建模，未整合产生行为的各类过程。我们主张，动物数字孪生应被视为具身动力系统，需统一神经活动、身体力学、感觉反馈与环境交互过程。我们提出一种小鼠神经肌肉骨骼数字孪生模型，结合X射线CT、高分辨率白光切片及Scx-GFP成像的多模态解剖重建结果，整合生物力学模拟、Bonhoeffer–van der Pol神经动力学与触觉反馈。该框架形成闭合的感觉运动环路，其中行为通过神经系统、肌肉骨骼系统与环境间的持续交互涌现。Bonhoeffer–van der Pol模型为这类交互的大规模模拟提供了计算易处理的动力学基础。神经肌肉骨骼数字孪生可成为计算神经科学、生物力学、人工智能与机器人学的交汇点；当与自适应学习和自主实验结合时，它们可从被动模拟发展为主动科学工具，生成假设、预测干预措施并指导实验。这类具身数字孪生可推进生物智能研究，并支持新型自适应生物医学与机器人系统的开发。
英文摘要
Digital twin technologies could transform neuroscience and biomedicine by creating predictive computational representations of living organisms. However, most animal digital twins model neural circuits, anatomy, or biomechanics separately rather than integrating the processes that generate behavior. We argue that animal digital twins should instead be conceived as embodied dynamical systems that unify neural activity, body mechanics, sensory feedback, and environmental interactions. We propose a neuro-musculoskeletal digital twin of the mouse that combines multimodal anatomical reconstruction from X-ray CT, high-resolution white-light sections, and Scx-GFP imaging with biomechanical simulation, Bonhoeffer--van der Pol neural dynamics, and tactile feedback. This framework forms a closed sensorimotor loop in which behavior emerges through continuous interactions among the nervous system, musculoskeletal system, and environment. The Bonhoeffer--van der Pol model provides a computationally tractable dynamical foundation for large-scale simulation of these interactions. Neuro-musculoskeletal digital twins could provide a convergence point for computational neuroscience, biomechanics, artificial intelligence, and robotics. When coupled with adaptive learning and autonomous experimentation, they may develop from passive simulations into active scientific instruments that generate hypotheses, predict interventions, and guide experiments. Such embodied digital twins could advance the study of biological intelligence and support new adaptive biomedical and robotic systems.
