Brinkman B A W, Yan H, Maffei A, Park I M, Fontanini A, Wang J, La Camera G
State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, People's Republic of China.
Appl Phys Rev. 2022 Mar;9(1):011313. doi: 10.1063/5.0062603.
Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.
皮层神经元会发出看似不规则的动作电位序列或“尖峰”，神经网络动力学则源自神经回路内协同的尖峰活动。这些丰富的动力学表现为多种模式，它们自发出现或响应感觉输入产生的传入活动。在本综述中，我们聚焦于一种神经动力学，它最好被理解为一系列多个离散隐藏状态的重复激活。这些短暂占据的状态被称为“亚稳态”，并已与重要的感觉和认知功能相关联。例如，在啮齿动物的味觉皮层中，亚稳态动力学与刺激编码、期望状态以及决策有关。在猕猴的额叶、顶叶和运动区域，亚稳态活动与行为表现、选择行为、任务难度和注意力有关。在本文中，我们回顾了神经亚稳态动力学的实验证据以及研究神经回路中亚稳态活动的理论方法。这些方法包括：(i) 基于非平衡统计物理的网络动力学理论框架；(ii) 从各种神经信号中提取有关亚稳态信息的统计方法；以及(iii) 受实验结果启发的最新神经网络方法，用于模拟亚稳态动力学的出现。通过讨论这些主题，我们旨在提供一个连贯的观点，即不同活动状态之间的转变如何为诸如感知、记忆、期望或决策等基本功能提供神经基础，更广泛地说，亚稳态神经活动的研究如何推进我们对健康和疾病状态下神经回路功能的理解。
