The standard engineering approach when facing uncertainty is modelling. Mixing data from a well-calibrated model with real recordings has led to breakthroughs in many applications of AI, from computer vision to autonomous driving. This type of model-based data augmentation is now beginning to show promising results in biosignal processing as well. However, while these simulated data are necessary, they are not sufficient for virtual neurophysiological experiments. Simply generating neural signals that reproduce a predetermined motor behaviour does not capture the flexibility, variability, and causal structure required to probe neural mechanisms during control tasks. In this study, we present an in silico neuromechanical model that combines a fully forward musculoskeletal simulation, reinforcement learning, and sequential, online electromyography synthesis. This framework provides not only synchronised kinematics, dynamics, and corresponding neural activity, but also explicitly models feedback and feedforward control in a virtual participant. In this way, online control problems can be represented, as the simulated human adapts its behaviour via a learned RL policy in response to a neural interface. For example, the virtual user can learn hand movements robust to perturbations or the control of a virtual gesture decoder. We illustrate the approach using a gesturing task within a biomechanical hand model, and lay the groundwork for using this technique to evaluate neural controllers, augment training datasets, and generate synthetic data for neurological conditions.

翻译：面对不确定性时，标准工程学方法是建模。将经过良好校准的模型数据与真实记录相结合，已在从计算机视觉到自动驾驶的众多人工智能应用领域取得突破性进展。这类基于模型的数据增强方法如今在生物信号处理领域也开始展现出令人瞩目的成果。然而，尽管这些模拟数据不可或缺，但对于虚拟神经生理学实验而言仍不充分。仅生成能复现预定运动行为的神经信号，无法捕捉控制任务中探究神经机制所需的灵活性、变异性和因果结构。本研究提出了一种硅基神经力学模型，该模型整合了完全前向肌肉骨骼仿真、强化学习以及序列化在线肌电信号合成。该框架不仅能提供同步的运动学、动力学及相应神经活动数据，还能在虚拟受试者中显式建模反馈与前馈控制机制。通过这种方式，当模拟人类通过习得的强化学习策略响应神经接口时，在线控制问题得以表征。例如，虚拟用户可学习抗干扰的手部运动或虚拟手势解码器的控制方法。我们通过在手部生物力学模型中进行手势任务演示了该方法，并为运用该技术评估神经控制器、增强训练数据集以及生成神经系统疾病的合成数据奠定了基础。