Collaborative Research: The Ever-Changing Network: How Changes in Architecture Shape Neural Computations

合作研究：不断变化的网络：架构的变化如何塑造神经计算

• 批准号: 1514743
• 负责人: Eric Shea-Brown
• 金额: $ 9万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514743&HistoricalAwards=false
• 关键词: Collaborative; Research; Ever; Changing; Network

Our brains are constantly changing. Experiences and memories leave their imprints on connections between neurons. Understanding this process is fundamental to understanding how the brain works. While this question has been of central importance to neuroscience for decades, at this moment researchers are well positioned to make significant progress -- new recording devices and imaging techniques are revealing the activity and changes within the networks of the brain at unprecedented scale and resolution. Sound mathematical models are essential to keep up with the mounting avalanche of data. The goal of this project is to develop mathematical tools to assist with improving understanding how networks of neurons are shaped by experiences. Developing this theory is crucial for understanding learning, as well as associated disorders. The project will focus on how learning improves the brain's ability to make decisions and store memories. Graduate students and postdocs joining this project will be part of an established, interdisciplinary mathematics research community. Trainees will gain a wide perspective of mathematical neuroscience through integrated research at three institutions, including extensive visits among them. This research project builds on earlier results of this team to address a central challenge in the mathematical analysis of biophysically realistic neuronal networks: How brain activity changes brain structure over time. Understanding neural computation demands a description of how network dynamics co-evolves with network architecture. The research team will address this challenge by answering specific questions about the interplay between spatiotemporal patterns of neural activity, the attendant changes in network architectures, and the resulting neural computations. This project focuses on two main questions. First, what mathematical techniques can describe the co-evolution of network dynamics and network connectivity toward stable assemblies of neurons? To address this question this project will build a theory describing how global network structure evolves under the dynamics of biophysically realistic plasticity rules that operate on the scale of individual spikes and synapses. Analysis of these models requires novel multiscale and averaging methods. The resulting equations allow analysis of the stability of network architectures and their dependence on stimulus drive. With these results, the second question can be addressed: How does network plasticity create spatiotemporal dynamics that support the basic building blocks of neural computation? Models to understand how plasticity forms networks whose dynamics underlie specific operations on incoming stimuli will be developed to address this question. The mechanism by which long-term plasticity can reshape the connectivity of a network to encode a precise temporal sequence of events will also be investigated.

我们的大脑在不断变化。经验和记忆会在神经元之间的连接上留下印记。理解这个过程是理解大脑如何工作的基础。虽然这个问题几十年来一直是神经科学的核心问题，但目前研究人员已经做好了取得重大进展的准备-新的记录设备和成像技术正在以前所未有的规模和分辨率揭示大脑网络内的活动和变化。健全的数学模型对于跟上数据雪崩的速度至关重要。该项目的目标是开发数学工具，以帮助更好地理解神经元网络是如何被经验塑造的。发展这一理论对于理解学习以及相关疾病至关重要。该项目将重点关注学习如何提高大脑做出决定和存储记忆的能力。研究生和博士后加入这个项目将是一个既定的，跨学科的数学研究社区的一部分。学员将通过在三个机构的综合研究，包括广泛的访问，获得数学神经科学的广泛视角。该研究项目建立在该团队早期的结果基础上，以解决生物物理学现实神经元网络数学分析中的一个核心挑战：大脑活动如何随着时间的推移改变大脑结构。理解神经计算需要描述网络动态如何与网络架构共同进化。研究小组将通过回答有关神经活动时空模式之间相互作用的具体问题来解决这一挑战，随之而来的网络架构变化以及由此产生的神经计算。该项目侧重于两个主要问题。首先，什么样的数学技术可以描述网络动力学和网络连通性向稳定的神经元组装的共同进化？为了解决这个问题，该项目将建立一个理论，描述全球网络结构如何在生物物理学上现实的可塑性规则的动态下演变，这些规则在单个尖峰和突触的规模上运作。这些模型的分析需要新的多尺度和平均方法。由此产生的方程允许分析的稳定性的网络架构和它们对刺激驱动的依赖。有了这些结果，第二个问题就可以解决了：网络可塑性如何创造时空动力学来支持神经计算的基本构建模块？模型来了解可塑性如何形成网络的动态基础上传入的刺激的具体操作将开发来解决这个问题。还将研究长期可塑性重塑网络连接以编码事件的精确时间序列的机制。