Robust spatiotemporal dynamics in multi-layer neuronal networks

多层神经元网络中鲁棒的时空动力学

• 批准号: 1615737
• 负责人: Zachary Kilpatrick
• 金额: $ 23.4万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1615737&HistoricalAwards=false
• 关键词: Robust; spatiotemporal; dynamics; multi; layer

To navigate in a constantly changing world, humans and other animals continually make decisions and store memories. Since the world is noisy and brain activity is highly variable, it is remarkable that organisms can perform cognitive tasks as accurately as they do. One feature of the human brain that may account for its exceptional computational ability is its modular structure. The entire network of the brain is organized into a collection of densely connected subnetworks, helping to localize certain neural computations. Studying the effects of this underlying structure could ultimately help in the analysis of increasingly large data sets collected by experimental neuroscientists. This project will consider the impact of modular structures in the brain, focusing on networks known to perform specific cognitive tasks like categorization, short-term memory, and spatial navigation. These computations are performed by networks that can represent spatial position, and the associated mathematical models often describe variables that change in space and time. This project will facilitate the development of new methods for studying how noise impacts the dynamics of neuronal networks with multiple temporal and spatial scales, specifically in networks with a multi-layered structure. This project will contribute to the national BRAIN initiative by identifying new computational tools for understanding the role of the brain's network architecture in cognition. Furthermore, trainees supported by this award will learn cutting-edge methods in statistics, nonlinear dynamics, and stochastic processes. These methods are broadly applicable to many fields in science that utilize large-scale data such as genetics, social science, and climatology.This research project addresses the problem of understanding how the multi-layered structure of many areas of the brain shape neural computation. This problem will be addressed in three main ways: (i) building multi-layer network models of observed brain circuits that process spatial information; (ii) developing mathematical tools for studying these equations to extract information about their dynamics; and (iii) corroborating this work with experimental collaborators that record and image propagating activity in brain tissue. The cognitive tasks of spatial navigation, spatial working memory, and visual input categorization will be studied. Spatial navigation requires the integration of both angular and linear self-motion cues, and models developed in the project will explore different ways multi-layered network architecture can dampen variability in position codes. The investigation of how multi-layered architectures with different scales of spatial heterogeneity can robustly represent spatial position will improve our insights into the function of spatial working memory. Experimental recordings from visual brain areas have found that interfaces between network layers modify the propagation of stimulus-related activity. The impact of this phenomenon on stimulus processing will be explored in detail using mathematical models of sensory brain activity. All these projects require the development of new tools for determining how noise influences multi-scale systems, metastability, and spatiotemporal patterns, of broad applicability to other scientific fields including epidemiology, systems biology, and ecology.

为了在不断变化的世界中航行，人类和其他动物不断做出决定并存储记忆。由于世界是嘈杂的，大脑活动是高度可变的，生物体能够像它们一样准确地执行认知任务是值得注意的。人类大脑的一个特征可以解释其非凡的计算能力，那就是它的模块化结构。大脑的整个网络被组织成密集连接的子网络的集合，帮助定位某些神经计算。研究这种潜在结构的影响最终可能有助于分析实验神经科学家收集的越来越大的数据集。该项目将考虑大脑中模块化结构的影响，重点关注已知执行特定认知任务的网络，如分类，短期记忆和空间导航。这些计算是由可以表示空间位置的网络执行的，相关的数学模型通常描述在空间和时间上变化的变量。该项目将促进新方法的开发，用于研究噪声如何影响具有多个时间和空间尺度的神经元网络的动态，特别是在具有多层结构的网络中。该项目将通过确定新的计算工具来理解大脑网络结构在认知中的作用，从而为国家BRAIN计划做出贡献。此外，获得该奖项支持的学员将学习统计、非线性动力学和随机过程领域的前沿方法。这些方法广泛适用于遗传学、社会科学、气候学等利用大规模数据的许多科学领域。本研究项目致力于了解大脑多个区域的多层结构如何塑造神经计算。这个问题将通过三种主要方式来解决：（i）建立观察到的处理空间信息的大脑回路的多层网络模型;（ii）开发用于研究这些方程的数学工具，以提取有关其动态的信息;以及（iii）与记录和成像脑组织中传播活动的实验合作者证实这项工作。本研究将探讨空间导航、空间工作记忆、视觉输入分类等认知任务。空间导航需要整合角度和线性自我运动线索，该项目开发的模型将探索多层网络架构抑制位置代码变化的不同方式。研究具有不同尺度空间异质性的多层结构如何鲁棒地表征空间位置，将有助于我们更深入地了解空间工作记忆的功能。来自视觉大脑区域的实验记录发现，网络层之间的接口修改了与刺激相关的活动的传播。这种现象对刺激处理的影响将使用感觉脑活动的数学模型进行详细探讨。所有这些项目都需要开发新的工具来确定噪声如何影响多尺度系统，亚稳态和时空模式，广泛适用于其他科学领域，包括流行病学，系统生物学和生态学。