Bayesian estimation of network connectivity and motifs

网络连接性和主题的贝叶斯估计

• 批准号: 9170679
• 负责人: DARIO L RINGACH
• 金额: $ 39.56万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-27 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170679
• 关键词: Accounting; Address; Algorithms; BRAIN initiative; Bayesian Analysis; Behavior; Biological; Biological Neural Networks; Calcium; Cataloging; Catalogs; Cells; Communities; Complex; Data; Databases; Detection; Development; Disease; Engineering; Formulation; Goals; Graph; Health; Image; In Vitro; Individual; Investigation; Knowledge; Learning; Light; Measures; Methods; Microscopy; Mus; Nature; Network-based; Neurons; Neurosciences; Noise; Opsin; Pattern; Population; Probability; Public Health; Publishing; Research; Resources; Science; Software Tools; Statistical Models; Synapses; System; Techniques; Testing; Time; Validation; Work; abstracting; analytical tool; area V1; area striata; cell type; hippocampal pyramidal neuron; improved; in vivo; innovative technologies; network models; neural circuit; novel; optogenetics; relating to nervous system; response; signal processing; social; tool; two-photon

Abstract The overarching goal of this proposal is to learn how large groups of neurons interact in a network to perform computations that go beyond the individual ability of each cell. Our working hypothesis is that emergent behavior in neural networks results from their organization into a hierarchy of modular sub-networks or motifs, each performing simpler computations than the network as a whole. This theoretical framework suggests that our understanding of neural networks will advance if we can reliably measure network connectivity, detect recurring motifs, elucidate the computations they perform, and reveal how these smaller modules are combined into larger networks capable of performing increasingly complex computations. To advance the field forward we will: (a) develop novel system identification methods for cortical networks based on dynamical, two- photon imaging data. Our methods will use a Bayesian formulation that incorporates prior constraints on network topology, sparsity of synaptic connections, and cell type, derived from published, experimental data; (b) advance graph theoretic methods to identify patterns of connectivity among subsets of neurons which appear at rates higher than chance; (c) will use extensive in-vivo and in-vitro methods to validate our techniques. The work will deliver transformative software tools for Bayesian inference of network connectivity from functional data; it will yield a catalog of elementary cortical motifs of excitatory and inhibitory cells that will shed light on the wiring of the cortical circuitry; and it will generate the first database combining functional calcium imaging data with “ground truth” estimates of direct synaptic connectivity. Altogether, the proposed work will make available much needed analytical tools and databases to support a wide range of studies under the BRAIN initiative.

摘要 这项提议的首要目标是了解大群神经元如何在网络中相互作用， 超越每个细胞的能力的计算。我们的工作假设是 神经网络中的行为源于它们被组织成模块化子网络或基元的层次结构， 每个节点执行比整个网络更简单的计算。这一理论框架表明， 如果我们能够可靠地测量网络的连通性， 重复出现的图案，阐明了它们执行的计算，并揭示了这些较小的模块是如何 组合成能够执行越来越复杂的计算的更大的网络。来推进这个领域 未来我们将：（a）开发新的系统识别方法的皮质网络的基础上动态，两个- 光子成像数据。我们的方法将使用贝叶斯公式，该公式包含以下先验约束： 网络拓扑结构，突触连接的稀疏性，和细胞类型，来自公开的实验数据; (b)先进的图论方法来识别神经元子集之间的连接模式， 出现率高于机会;（c）将使用广泛的体内和体外方法来验证我们的 技术.这项工作将提供变革性的软件工具，用于网络连接的贝叶斯推理 从功能数据;它将产生一个兴奋性和抑制性细胞的基本皮质基序目录， 揭示了皮层电路的布线;它将产生第一个数据库， 钙成像数据与直接突触连接的“基本事实”估计。总的来说， 工作将提供急需的分析工具和数据库，以支持根据 大脑倡议。