Understanding Selective Recruitment in Neuronal Networks via Systems Theory

通过系统理论理解神经网络中的选择性招募

• 批准号: 1826065
• 负责人: Jorge Cortes
• 金额: $ 37.33万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826065&HistoricalAwards=false
• 关键词: Understanding; Selective; Recruitment; Neuronal; Networks

This project will study the dynamics of systems characterized by a particular interconnection structure, inspired by properties of nerve cells. Nerve cell models typically implement some type of activation function, which determines how the nerve cell responds to input stimuli from other nerve cells. The activation function studied in this project passes an input signal without changes, if the input to the structure is positive. However, if the input to the activation function is negative, then the output is zero. This activation function gives rise to a model of nerve cell dynamics that is capable of accurately representing some behaviors of real neurons. There are powerful mathematical results available for these models. In contrast, nerve cell models with activation functions based on exponentials can be difficult to analyze. This project will create new mathematical tools to study how different subnetworks interact, either to suppress unwanted activity or to synchronize and reinforce their responses. These subnetworks can be built up hierarchically into progressively more complex structures. The project will relate these networks to both normal and abnormal neuronal circuits. The results will be of interest for the insight they provide into the architecture of the human brain, but also for constructing explainable engineered networks that can learn and adapt. This project will advance the national health and welfare by increasing understanding of biological neuronal systems, and by enabling the creation of robotic systems with enhanced resilience and adaptabilty. The project will also impact the training of a new generation of undergraduate and graduate students through undergraduate student involvement in research, graduate supervision and curriculum development, outreach targeted at middle, high school, incoming freshman and transfer students, involvement and retention of minority students, and broad dissemination activities.This project addresses the existing gap between the experimental evidence on selective recruitment in the brain and the theoretical understanding of the mechanisms that explain it. Taking a systems and controls perspective, the research effort aims to develop a mathematical framework to analyze how neuronal networks optimize their computational capabilities and unravel the role that network structure plays in shaping the dynamical behavior of the brain. This research seeks to advance the current knowledge on how the brain selects the relevant subnetworks during each time interval and suppresses the activity of others, and how it efficiently recruits those selected subnetworks and coordinates information transfer to and from them. The research plan is structured along the following interconnected research thrusts: (i) network stability and emergence of oscillations. The project seeks an in-depth characterization of the conditions on network structure that determine its dynamical properties, in particular, existence, uniqueness, and asymptotic stability of equilibrium points and limit cycles; (ii) inhibitory control and dynamic dimensionality. The project aims to characterize the mechanisms by which long-range connections between two networks can be used by one to dynamically inhibit different subset of nodes in the other. An aspect of particular importance is the consideration of physiologically-relevant scenarios involving feedforward and feedback inhibition, and their relation to network size, structure, and robustness; (iii) inter-regional connectivity and information transfer. Building on the lessons learned in the previous two thrusts, the project sets out to characterize the mechanisms of information transfer through coherent oscillations. This involves the study of ways in which neuronal networks can encode and decode information as oscillations and the role that the hierarchical structure and inter-regional connectivity of the brain has on them.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将研究以特定的互连结构为特征的系统动力学，灵感来自神经细胞的特性。神经细胞模型通常实现某种类型的激活函数，这决定了神经细胞对其他神经细胞的输入刺激的反应。如果结构的输入为正，则该项目中研究的激活函数通过输入信号而无需更改。但是，如果激活函数的输入为负，则输出为零。该激活函数产生了神经细胞动力学模型，该模型能够准确地表示真实神经元的某些行为。这些模型有强大的数学结果。相反，基于指数的激活功能的神经细胞模型可能很难分析。该项目将创建新的数学工具来研究不同的子网相互作用，以抑制不必要的活动或同步和加强其响应。这些子网可以从层次上构建为逐渐复杂的结构。该项目将将这些网络与正常和异常的神经元电路联系起来。结果将引起人们对人脑体系结构的洞察力的关注，而且还用于构建可以学习和适应的可解释的工程网络。该项目将通过增加对生物神经系统的了解，并通过增强弹性和适应性的机器人系统来提高国家健康和福利。该项目还将通过研究生参与研究，研究生监督和课程发展，针对中学，高中，新生和转学学生，参与和保留少数派学生的参与和保留培训，对现有的差异性证据进行招聘，对招聘的机制介绍了招聘的培训，该项目将影响新一代的本科生和研究生培训。借助系统并控制观点，研究工作旨在开发一个数学框架，以分析神经元网络如何优化其计算能力，并揭示网络结构在塑造大脑动态行为方面所扮演的作用。这项研究旨在促进有关大脑在每个时间间隔中如何选择相关子网的当前知识，并抑制他人的活动，以及它如何有效地募集那些选定的子网并协调向他们传输的信息转移。 该研究计划沿以下相互联系的研究构成：（i）网络稳定性和振荡的出现。该项目寻求对网络结构条件的深入表征，这些条件决定了其动力学特性，特别是存在，唯一性和平衡点的渐近稳定性和限制周期； （ii）抑制控制和动态维度。该项目旨在表征一个机制，通过一个机制可以使用两个网络之间的长距离连接来动态抑制另一个节点的不同子集。一个特别重要的一个方面是考虑涉及进料和反馈抑制的生理相关场景及其与网络大小，结构和鲁棒性的关系； （iii）区域间连通性和信息传输。该项目以前两个推力中学到的教训为基础，以表征通过连贯振荡的信息传输机制。这涉及研究神经元网络可以编码和解码信息为振荡的方式，以及大脑对大脑的层次结构和区域间连通性的作用。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力和更广泛的影响来评估的支持，并被认为值得通过评估的支持。

项目成果

Heterogeneity of central nodes explains the benefits of time-varying control scheduling in complex dynamical networks

• DOI: 10.1093/comnet/cnz001
• 发表时间: 2019-02
• 期刊: J. Complex Networks
• 作者: Erfan Nozari;F. Pasqualetti;J. Cortés

Hierarchical Selective Recruitment in Linear-Threshold Brain Networks Part II: Multilayer Dynamics and Top-Down Recruitment

• DOI: 10.1109/tac.2020.2997854
• 发表时间: 2021-03-01
• 期刊: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• 影响因子: 6.8
• 作者: Nozari，Erfan;Cortes，Jorge
• 通讯作者: Cortes，Jorge

Selective Recruitment in Hierarchical Complex Dynamical Networks with Linear-Threshold Rate Dynamics

• DOI: 10.1109/cdc.2018.8618734
• 发表时间: 2018-12
• 期刊: 2018 IEEE Conference on Decision and Control (CDC)
• 作者: Erfan Nozari;J. Cortés

Oscillations and coupling in interconnections of two-dimensional brain networks

二维大脑网络互连中的振荡和耦合

• 发表时间: 2019
• 期刊: Proceedings of the American Control Conference
• 作者: E. Nozari, J. Cortés

Energy-Transfer Edge Centrality and Its Role in Enhancing Network Controllability

能量传输边缘中心性及其在增强网络可控性中的作用

• DOI: 10.1109/tnse.2020.3038309
• 发表时间: 2021
• 期刊: IEEE Transactions on Network Science and Engineering
• 影响因子: 6.6
• 作者: Chanekar, Prasad Vilas;Nozari, Erfan;Cortes, Jorge
• 通讯作者: Cortes, Jorge