Bridging structure, dynamics, and information processing in brain networks

哺乳动物的大脑被认为是为健壮和适应性强的计算而设计的最佳方案 来自世界的感觉输入，关于其硬件(网络结构)和软件(网络 动力学)。错综复杂的结构连接和丰富的网络之间的精确联系 然而，动力学还是个未知数。此外，我们对网络结构和网络如何 大脑网络中潜在的编码原理的动态形状(或由其形成)是有限的。我的研究 Plan建议通过利用由最先进的实验技术获得的丰富数据集来缩小这一差距 艾伦脑科学研究所和创新的数学方法。 具体地说，我的项目旨在1)将网络结构与大脑中的动态信息处理联系起来， 以及2)弥合详细的生物生理机制和主要的神经编码之间的差距 原理，重点是预测编码理论，使用数据驱动的数学模型。为了实现目标1， 我将研究如何通过可同步性、亚稳定和集成来衡量网络动态 信息取决于网络的局部和全局结构。然后我会研究是否在实验上 得到的小鼠脑连接体具有独特的动力学特性，具有最优的连接性结构。 这些分析将扩展到细胞类型和特定于层的大脑连接，基于最新的Allen 从Cre转基因小鼠获得的小鼠脑连接数据。在独立阶段，我将 研究类脑网络是否可以从动态测量的优化进化而来。关于AIM 2、我将分析从我当前的协作项目中获得的数据，该项目将对预测进行实验测试 对小鼠视觉皮质中的模型进行编码。在这项研究中，我们测量了神经活动对预期的反应 以及三个层级相关区域的意外自然刺激序列。在完成 实验，在指导阶段，我将研究预测编码中算法单元的映射 不同层次的神经元群的模型。在我独立的职业生涯期间，我将延长 结合主动感知和丘脑-皮质回路的预测编码模型。 该项目在指导阶段将在华盛顿大学进行，该大学提供 高度跨学科的环境，为我成为一名独立研究人员提供了理想的培训。我 还将获得丰富的资源和艾伦脑科学研究所的杰出合作者。我 将有两位导师，一位来自华盛顿大学，另一位来自艾伦大脑研究所 科学。这一独特的设置将使我能够根据获得的实验数据研究数学模型 在具有深厚理论背景的导师的指导下，采用尖端技术。带着理论 与实验密切相关，我相信我提出的项目将有助于我们对这种联系的理解 在神经网络的结构和计算之间，解决大脑主动性的高优先级。

The mammalian brain is believed to be optimally designed for robust and adaptable computation of the sensory inputs from the world, with respect to both its hardware (network structure) and software (network dynamics). The precise connections between the intricate structural connectivity and the rich network dynamics, however, are yet unknown. Moreover, our understanding of how the network structure and dynamics shape (or are shaped by) underlying coding principles in the brain network, is limited. My research plan proposes to close this gap by leveraging rich dataset obtained by state-of-art experimental techniques at the Allen Institute for Brain Science and innovative mathematical methods. Specifically, my project aims to 1) link network structure and dynamic information processing in the brain, and to 2) bridge the gap between detailed biophysiological mechanisms and overarching neural coding principles with a focus on predictive coding theory, using data-driven mathematical models. To address Aim 1, I will investigate how network dynamics measured by synchronizability, metastability, and integrated information depend on local and global structure of the network. I will then study whether the experimentally obtained mouse brain connectome has optimal connectivity structures for unique dynamical characteristics. These analyses will be extended to the cell-type and layer-specific brain connectivity, based on the latest Allen Mouse Brain Connectivity data obtained from Cre-transgenic mice. During the independent phase, I will investigate whether brain-like networks can be evolved from optimization of dynamic measures. Regarding Aim 2, I will analyze data obtained from my current collaborative project which experimentally tests predictive coding models in the mouse visual cortex. In this study, we measure neural activity in response to expected and unexpected sequences of natural stimuli across three hierarchically related areas. Upon completion of the experiments, during the mentored phase, I will investigate mapping of algorithmic units in predictive coding models to neuronal populations in different layers. During my independent career period, I will extend the predictive coding model to incorporate active sensing and thalamo-cortical circuitries. The project during the mentored phase will be carried out at the University of Washington which provides a highly interdisciplinary environment and offers the ideal training for me to become an independent researcher. I will also have access to rich resources and outstanding collaborators at the Allen Institute for Brain Science. I will have two mentors, one from the University of Washington and another from the Allen Institute for Brain Science. This unique setup will allow me to study mathematical models based on experimental data obtained by cutting-edge techniques with guidance from mentors with strong theoretical backgrounds. With theories closely tied to experiments, I believe my proposed project will contribute to our understanding of the connection between structure and computation of the neuronal network, addressing BRAIN initiative’s high priorities.