Bridging structure, dynamics, and information processing in brain networks

哺乳动物的大脑被认为是最佳设计的，用于对大脑进行鲁棒和适应性强的计算。 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）。复杂的结构连接和丰富的网络之间的精确连接 然而，动态尚不清楚。此外，我们对网络结构和 动力学塑造（或塑造）大脑网络中的潜在编码原则是有限的。我的研究 该计划建议通过利用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。 具体来说，我的项目旨在1）连接网络结构和大脑中的动态信息处理， 以及2）在详细的生物生理学机制和总体神经编码之间架起差距的桥梁 本书使用数据驱动的数学模型，以预测编码理论为重点。为了实现目标1， 我将研究如何通过同步性，亚稳定性和综合性来衡量网络动态。 信息依赖于网络的局部和全局结构。然后，我将研究实验是否 获得的小鼠脑连接体具有最佳的连接结构，具有独特的动力学特性。 这些分析将扩展到细胞类型和特定层的大脑连接，基于最新的艾伦 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中进化出来。关于Aim 2.我将分析从我目前的合作项目中获得的数据，该项目通过实验测试预测 小鼠视觉皮层的编码模型。在这项研究中，我们测量神经活动，以响应预期的 以及三个等级相关区域的意外自然刺激序列。完成后 实验，在指导阶段，我将研究预测编码中算法单元的映射 不同层的神经元群体的模型。在我独立的职业生涯期间，我将延长 预测编码模型结合主动感知和丘脑-皮层电路。 该项目在辅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，并为我成为一名独立的研究人员提供了理想的培训。我 还将接触到艾伦脑科学研究所的丰富资源和杰出合作者。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 科学这种独特的设置将使我能够研究基于实验数据的数学模型 通过尖端技术，并在具有强大理论背景的导师的指导下，在科学理论 与实验密切相关，我相信我提出的项目将有助于我们理解这种联系 神经元网络的结构和计算之间的联系，解决了BRAIN倡议的高优先级。

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.