混合大脑领域：人脑与人工智能
In the realm of hybrid Brain: Human Brain and AI
AI总结：
本文提出受大脑启发的脑机接口（BI-BCIs），将脉冲神经网络与神经形态硬件结合，用于解码多模态神经信号、建模大脑信息处理并提升BCI稳定性与效率。
摘要：
随着神经科学和工程学的最新发展，现在已有可能记录并解码大脑信号。同时，越来越多的刺激方法已经出现，用于调节和影响大脑活动。当前的脑机接口（BCI）技术主要关注治疗结果，它已经证明了其作为严重运动障碍患者的辅助和康复技术的有效性。最近，人工智能（AI）和机器学习（ML）技术已被用于解码大脑信号。除了这一进展之外，将AI与以植入式神经技术形式出现的先进BCI相结合，为神经系统和精神疾病的诊断、预测和治疗提供了新的可能性。在这一背景下，我们设想开发闭环、智能、低功耗和小型化的神经接口，这些接口将使用受大脑启发的AI技术和神经形态硬件来处理来自大脑的数据。这将被称作受大脑启发的脑机接口（BI-BCIs）。此类神经接口将提供对更深层大脑区域的访问，并更好地理解大脑的功能和工作机制，从而提高BCI的操作稳定性和系统效率。一方面，以脉冲神经网络（SNNs）为代表的受大脑启发的AI算法将被用于解释BCI系统中的多模态神经信号。另一方面，由于SNNs能够捕捉生物神经元丰富的动力学特性，并能够表示和整合时间、频率和相位等不同信息维度，它将被用于建模和编码大脑中的复杂信息处理，并向用户提供反馈。本文概述了与大脑交互的不同方法，展示了未来应用，并讨论了AI与BCI的融合。
英文摘要：
With the recent developments in neuroscience and engineering, it is now possible to record brain signals and decode them. Also, a growing number of stimulation methods have emerged to modulate and influence brain activity. Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes, it already demonstrated its efficiency as assistive and rehabilitative technology for patients with severe motor impairments. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been used to decode brain signals. Beyond this progress, combining AI with advanced BCIs in the form of implantable neurotechnologies grants new possibilities for the diagnosis, prediction, and treatment of neurological and psychiatric disorders. In this context, we envision the development of closed loop, intelligent, low-power, and miniaturized neural interfaces that will use brain inspired AI techniques with neuromorphic hardware to process the data from the brain. This will be referred to as Brain Inspired Brain Computer Interfaces (BI-BCIs). Such neural interfaces would offer access to deeper brain regions and better understanding for brain's functions and working mechanism, which improves BCIs operative stability and system's efficiency. On one hand, brain inspired AI algorithms represented by spiking neural networks (SNNs) would be used to interpret the multimodal neural signals in the BCI system. On the other hand, due to the ability of SNNs to capture rich dynamics of biological neurons and to represent and integrate different information dimensions such as time, frequency, and phase, it would be used to model and encode complex information processing in the brain and to provide feedback to the users. This paper provides an overview of the different methods to interface with the brain, presents future applications and discusses the merger of AI and BCIs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
