The integration of the Metaverse into a human-centric ecosystem has intensified the need for sophisticated Human Digital Twins (HDTs) that are driven by the multifaceted human data. However, the effective construction of HDTs faces significant challenges due to the heterogeneity of data collection devices, the high energy demands associated with processing intricate data, and concerns over the privacy of sensitive information. This work introduces a novel biologically-inspired (bio-inspired) HDT framework that leverages Brain-Computer Interface (BCI) sensor technology to capture brain signals as the data source for constructing HDT. By collecting and analyzing these signals, the framework not only minimizes device heterogeneity and enhances data collection efficiency, but also provides richer and more nuanced physiological and psychological data for constructing personalized HDTs. To this end, we further propose a bio-inspired neuromorphic computing learning model based on the Spiking Neural Network (SNN). This model utilizes discrete neural spikes to emulate the way of human brain processes information, thereby enhancing the system's ability to process data effectively while reducing energy consumption. Additionally, we integrate a Federated Learning (FL) strategy within the model to strengthen data privacy. We then conduct a case study to demonstrate the performance of our proposed twofold bio-inspired scheme. Finally, we present several challenges and promising directions for future research of HDTs driven by bio-inspired technologies.

翻译：元宇宙融入以人为中心的生态系统，加剧了对基于多维度人类数据驱动的复杂人类数字孪生（HDT）的需求。然而，由于数据采集设备的异构性、处理复杂数据的高能耗需求以及敏感信息的隐私问题，HDT的有效构建面临重大挑战。本研究提出了一种新颖的生物启发（仿生）HDT框架，该框架利用脑机接口（BCI）传感器技术捕获脑信号作为构建HDT的数据源。通过采集和分析这些信号，该框架不仅减少了设备异构性并提高了数据采集效率，还为构建个性化HDT提供了更丰富、更精细的生理与心理数据。为此，我们进一步提出了一种基于脉冲神经网络（SNN）的生物启发神经形态计算学习模型。该模型利用离散的神经脉冲来模拟人脑处理信息的方式，从而在降低能耗的同时提升系统处理数据的效能。此外，我们在模型中集成了联邦学习（FL）策略以增强数据隐私保护。随后，我们通过案例研究展示了所提出的双重生物启发方案的性能。最后，我们针对生物启发技术驱动的HDT未来研究提出了若干挑战与潜在发展方向。