Robust spatiotemporal dynamics in multi-layer neuronal networks

为了在不断变化的世界中航行，人类和其他动物不断地做出决定并储存记忆。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。人脑的一个特征可能是它的模块化结构，这可以解释它非凡的计算能力。大脑的整个网络被组织成密集连接的子网络的集合，有助于定位某些神经计算。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。这个项目将考虑大脑中模块化结构的影响，重点关注已知的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中变化的变量。该项目将促进研究噪声如何影响具有多个时空尺度的神经网络动力学的新方法的发展，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，从而为国家大脑计划做出贡献。此外，该奖项支持的学员将学习统计学、非线性动力学和随机过程的前沿方法。这些方法广泛适用于许多利用大规模数据的科学领域，如遗传学、社会科学和气候学。这个研究项目解决了理解大脑许多区域的多层结构如何塑造神经计算的问题。这个问题将通过三种主要方式解决：(i)建立处理空间信息的观察脑回路的多层网络模型；（ii）开发研究这些方程的数学工具，以提取有关其动力学的信息；（三）与记录和成像脑组织传播活动的实验合作者证实这一工作。空间导航、空间工作记忆和视觉输入分类的认知任务将被研究。空间导航需要整合角度和线性自运动线索，项目中开发的模型将探索多层网络架构抑制位置代码可变性的不同方式。研究具有不同空间异质性尺度的多层结构如何有效表征空间位置，将有助于我们对空间工作记忆功能的认识。来自视觉脑区的实验记录发现，网络层之间的接口改变了刺激相关活动的传播。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，这些工具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。

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.