The Role of Decaying Inhibition in Forced Networks of Neurons

衰变抑制在神经元强制网络中的作用

• 批准号: 1225647
• 负责人: Nancy Kopell
• 金额: $ 52.5万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1225647&HistoricalAwards=false
• 关键词: Role; Decaying; Inhibition; Forced; Networks

Forced networks of oscillating neurons are ubiquitous in the nervous system. Although forced unitary oscillators have been heavily studied, there are very few studies of the effects of periodic forcing on networks of oscillators with multiple internal degrees of freedom. Understanding such systems is an essential step in understanding how the brain makes use of its rhythmic dynamics to communicate signals among regions. The networks in this project have an almost universal property of networks of neurons: there is exponentially decaying inhibition that feeds back to the excitatory cells of the network. In these projects, the decaying nature of the inhibition is important for the emergence of lower-dimensional dynamics and the ability to produce and analyze simplified models of network phenomena. The initial three subprojects concern noise, multiple periodic inputs, and intrinsic currents providing extra timescales that interact with periodic forcing. The work will relate the properties of network interaction, such as the decaying inhibition, to geometric ideas of invariant manifold theory. The aim is to produce a level of mathematical clarity that is not generally a part of initial modeling work, and to expand the set of tools and concepts available for further study of forced networks. The project focuses on networks producing gamma (30-80 Hz) oscillations and beta (12-30 Hz) oscillations, with physiological descriptions of the neurons appropriate to those rhythms.The dynamics of the nervous system are central to cognitive function, but how the brain makes use of its dynamics is barely beginning to be understood. Rhythms of the nervous system have long been known to be highly associated with cognitive processes including sensory coding, attention, learning, memory, and motor planning. The study of such rhythms, and their use in brain communication, is an excellent bridge between physiology and function. This project deals with the response of networks of neurons to periodic and other input, as would be seen from signals arriving from other parts of the brain. The purpose of the project is to understand how brain networks process their temporally and spatially coded inputs. This work is being done in the context of a broad initiative to study the origin and significance of brain rhythms. Kopell is the founder and current head of the Cognitive Rhythms Collaborative, a group of over two dozen Boston-based senior investigators interested in the physiological and dynamical mechanisms of neural activity, their importance in cognition, and their pathology in neurological diseases including schizophrenia, Parkinson's disease and epilepsy, as well as their changes in anesthesia; it includes experimentalists working in vivo and in vitro, mathematicians, statisticians and medical personnel, who are already incorporating ideas from the mathematical research into clinical practice.

振荡神经元的强迫网络在神经系统中无处不在。虽然强迫么正振子已被大量研究，但关于周期强迫对具有多个内部自由度的振子网络的影响的研究很少。了解这类系统是理解大脑如何利用其节律动力学在区域之间传递信号的关键一步。这个项目中的网络具有神经元网络的一个几乎普遍的特性：存在指数衰减的抑制，并反馈到网络的兴奋细胞。在这些项目中，抑制的衰减性质对于低维动力学的出现以及产生和分析网络现象的简化模型的能力是重要的。最初的三个子项目涉及噪声、多个周期性输入和提供与周期性强迫相互作用的额外时间尺度的本征电流。这项工作将把网络相互作用的性质，如衰减抑制，与不变流形理论的几何思想联系起来。其目的是提供一般不属于初始建模工作一部分的数学清晰度，并扩大可用于进一步研究强迫网络的工具和概念集。该项目专注于产生伽马(30-80赫兹)振荡和贝塔(12-30赫兹)振荡的网络，对神经元的生理描述与这些节律相适应。神经系统的动力学是认知功能的核心，但大脑如何利用其动力学才刚刚开始被理解。神经系统的节律长期以来一直被认为与认知过程高度相关，包括感觉编码、注意力、学习、记忆和运动规划。对这种节律的研究及其在大脑交流中的应用，是生理和功能之间的一座极好的桥梁。这个项目涉及神经元网络对周期性和其他输入的反应，从来自大脑其他部位的信号可以看出这一点。该项目的目的是了解大脑网络如何处理其时间和空间编码的输入。这项工作是在研究大脑节律的起源和意义的广泛倡议的背景下进行的。科佩尔是认知节律合作组织的创始人和现任负责人，该组织总部设在波士顿，由20多名高级研究人员组成，对神经活动的生理和动力学机制、它们在认知中的重要性、它们在精神分裂症、帕金森病和癫痫等神经疾病中的病理以及它们在麻醉中的变化感兴趣；它包括在体内和体外工作的实验者、数学家、统计学家和医务人员，他们已经将数学研究的想法融入临床实践。