Architecture for robust spatiotemporal dynamics in neuronal networks

神经网络中鲁棒时空动力学的架构

• 批准号: 1311755
• 负责人: Zachary Kilpatrick
• 金额: $ 18.49万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1311755&HistoricalAwards=false
• 关键词: Architecture; robust; spatiotemporal; dynamics; neuronal

A major challenge in neuroscience is to understand how the brain reliably completes cognitive tasks, despite a great deal of intrinsic variability. Cognitive tasks often require coordinated spatiotemporal activity, which experimentalists can measure using multiple electrodes, voltage sensitive dye, and optical techniques. The main goal of this research project is to develop mathematical tools to study how spatially structured networks in the brain produce reliable activity in the face of noise. Specifically, we will be interested in how the spatial architecture of synaptic connections interacts with noise on multiple scales to influence network activity. Thus, linking our theory with experimental data will require the development of new techniques in stochastic and nonlinear analysis, multiscale methods, and symmetric bifurcation theory. Neuronal network models that incorporate space often assume synaptic connectivity depends only on the distance between neurons. Such dynamical systems tend to be marginally stable, so solutions diffuse in the presence of noise. More realistic models of neuronal connectivity do not produce such degeneracies. We propose to examine how spatial heterogeneity can improve or limit the robustness of neuronal networks. We will analyze models that encode neural activity linked to working memory, decision making, and place localization. Our work will contribute mathematical methods to other current research areas in math biology concerned with analyzing the effect of noise on spatially extended systems. Everyday, humans make decisions, use memory, and navigate their environment. Spatially structured activity in the brain underlies all these processes. Spatially localized "bumps" of activity are thought to encode short term memories of spatial position. Propagating waves of activity represent visual inputs and the movement of limbs. These activity patterns must be generated in networks of the brain that are fraught with noise. Despite the noisiness of the brain, we execute cognitive tasks faithfully. How does this happen? We will develop mathematical techniques to address this major question. Mainly, we will explore how neuronal networks can be structured to support robust spatiotemporal dynamics. Insight into the robustness of cognitive processes can be used to develop therapeutic solutions for various mental disorders. Alzheimer's, dementia, and Parkinson's present more commonly as human lifespans grow. Treatments are not possible without well-developed theory. Our analysis of spatiotemporal neural coding will also help understanding of how spatial arrays of multielectrodes should best be designed for applications like neural prosthetics.

神经科学的一个主要挑战是了解大脑如何可靠地完成认知任务，尽管存在很大的内在变异性。认知任务通常需要协调的时空活动，实验人员可以使用多个电极、电压敏感染料和光学技术来测量这些活动。该研究项目的主要目标是开发数学工具来研究大脑中的空间结构网络如何在面对噪音时产生可靠的活动。具体来说，我们将感兴趣的是突触连接的空间结构如何与多个尺度上的噪声相互作用以影响网络活动。因此，将我们的理论与实验数据联系起来需要开发随机和非线性分析、多尺度方法和对称分岔理论方面的新技术。包含空间的神经元网络模型通常假设突触连接仅取决于神经元之间的距离。这种动态系统往往是边际稳定的，因此解决方案在存在噪声的情况下会扩散。更现实的神经元连接模型不会产生这种退化。我们建议研究空间异质性如何提高或限制神经元网络的鲁棒性。我们将分析编码与工作记忆、决策和地点定位相关的神经活动的模型。我们的工作将为数学生物学中当前涉及分析噪声对空间扩展系统的影响的其他研究领域贡献数学方法。人类每天都会做出决定、使用记忆并驾驭环境。大脑中的空间结构活动是所有这些过程的基础。活动的空间局部“颠簸”被认为编码了空间位置的短期记忆。传播的活动波代表视觉输入和四肢的运动。这些活动模式必须在充满噪音的大脑网络中产生。尽管大脑很吵闹，我们仍然忠实地执行认知任务。这是怎么发生的？我们将开发数学技术来解决这个主要问题。主要是，我们将探索如何构建神经元网络来支持强大的时空动态。对认知过程稳健性的洞察可用于开发各种精神障碍的治疗解决方案。随着人类寿命的延长，阿尔茨海默病、痴呆症和帕金森病的发病率越来越高。如果没有完善的理论，治疗是不可能的。我们对时空神经编码的分析也将有助于理解如何最好地为神经修复等应用设计多电极的空间阵列。