Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, GD, P.R. China.
Adv Neurobiol. 2024;41:63-87. doi: 10.1007/978-3-031-69188-1_3.
The human brain, as a complex system, has long captivated multidisciplinary researchers aiming to decode its intricate structure and function. This intricate network has driven scientific pursuits to advance our understanding of cognition, behavior, and neurological disorders by delving into the complex mechanisms underlying brain function and dysfunction. Modelling brain dynamics using machine learning techniques deepens our comprehension of brain dynamics from a computational perspective. These computational models allow researchers to simulate and analyze neural interactions, facilitating the identification of dysfunctions in connectivity or activity patterns. Additionally, the trained dynamical system, serving as a surrogate model, optimizes neurostimulation strategies under the guidelines of control theory. In this chapter, we discuss the recent studies on modelling and controlling brain dynamics at the intersection of machine learning and control theory, providing a framework to understand and improve cognitive function, and treat neurological and psychiatric disorders.
人类大脑作为一个复杂系统，长期以来一直吸引着多学科研究人员，他们旨在解码其复杂的结构和功能。这个错综复杂的网络推动了科学研究的发展，使我们能够深入了解大脑功能和功能障碍的复杂机制，从而加深我们对认知、行为和神经紊乱的理解。使用机器学习技术对大脑动力学进行建模，从计算的角度加深了我们对大脑动力学的理解。这些计算模型允许研究人员模拟和分析神经相互作用，从而有助于识别连接或活动模式中的功能障碍。此外，经过训练的动力系统作为替代模型，根据控制理论的指导优化神经刺激策略。在本章中，我们讨论了机器学习和控制理论交叉点上的大脑动力学建模和控制的最新研究，为理解和改善认知功能以及治疗神经和精神疾病提供了一个框架。
