CIF: Medium: Discovering Changes in Networks: Fundamental Limits, Efficient Algorithms, and Large-Scale Neuroscience

CIF：中：发现网络的变化：基本限制、高效算法和大规模神经科学

基本信息

批准号：
1955981
负责人：
Bobak Nazer
金额：
$ 123.2万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955981&HistoricalAwards=false
关键词：
CIF Medium Discovering Changes Networks

项目摘要

Modern technological advances have made it possible to collect extremely large datasets across a wide range of disciplines, spanning from social science to neuroscience. These datasets often consist of the activity patterns of many nodes that together form a network. For instance, in a social network, each node can represent a person while a connection between two nodes can represent a friendship. In the brain, each node can represent a neuron and a connection between two nodes can represent a link between the neurons. An emerging challenge is to design algorithms that can reliably and efficiently infer the hidden structure of these networks (namely the set of connections between nodes), given only recordings of the nodes' activity patterns. A related problem seeks to identify hidden clusters or communities in a network based only on the knowledge of some of the nodes' connections. This project seeks to examine these problems from the perspective of network change discovery: Rather than attempt to recover the full network structure, the goal is to determine whether a network structure has changed significantly over a certain time scale, and where these structural changes have occurred. Preliminary findings show that, in some settings, change discovery can be substantially easier than structure recovery. This cross-disciplinary project will examine network change discovery problems from theoretical and algorithmic perspectives; the resulting tools will be applied to large neural datasets, on the way to understand how learning a task changes the connectivity of neurons in a particular region of the brain. In addition, this project will also attempt to forge new connections between the information sciences and neuroscience through a combination of focused workshops, course development, and co-supervision of undergraduate and graduate research.From a technical perspective, the goals of the project are grouped into three thrusts. The first thrust employs modern tools from information theory and high-dimensional statistics to determine the fundamental limits for testing and recovering network changes. This effort will begin with simple canonical models, such as stochastic block models and Markov random fields, where direct comparisons are possible with prior work on structure learning. It will then move towards richer models that include dynamics, partial observations, and overlapping communities. The second thrust examines these problems from an algorithmic perspective, and seeks to design computationally-efficient algorithms that can provably approach the fundamental limits established in the first thrust. These algorithms will be validated on synthetic datasets, and then adapted to handle the complexities present in real datasets. The third thrust will apply these algorithms to large-scale calcium imaging neural datasets collected from the hippocampus of mice during association and extinction learning experiments. The goal is to determine how the connectivity between neurons changes as mice learn the task.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代技术的进步使得收集从社会科学到神经科学等广泛学科的极其庞大的数据集成为可能。这些数据集通常由许多节点的活动模式组成，这些节点一起形成一个网络。例如，在社交网络中，每个节点可以代表一个人，而两个节点之间的连接可以代表友谊。在大脑中，每个节点可以代表一个神经元，两个节点之间的连接可以代表神经元之间的链接。一个新出现的挑战是设计算法，在仅给出节点活动模式的记录的情况下，能够可靠且有效地推断这些网络的隐藏结构（即节点之间的连接集）。一个相关问题旨在仅基于某些节点连接的知识来识别网络中隐藏的集群或社区。该项目试图从网络变化发现的角度来审视这些问题：目标不是试图恢复完整的网络结构，而是确定网络结构在一定时间范围内是否发生了显着变化，以及这些结构变化发生在哪里。初步研究结果表明，在某些情况下，变更发现比结构恢复要容易得多。这个跨学科项目将从理论和算法的角度研究网络变化发现问题；由此产生的工具将应用于大型神经数据集，以了解学习任务如何改变大脑特定区域神经元的连接性。此外，该项目还将尝试通过重点研讨会、课程开发以及本科生和研究生研究的共同监督相结合，在信息科学和神经科学之间建立新的联系。从技术角度来看，该项目的目标分为以下几类：分为三个主力。第一个推动力采用信息论和高维统计的现代工具来确定测试和恢复网络变化的基本限制。这项工作将从简单的规范模型开始，例如随机块模型和马尔可夫随机场，其中可以与结构学习的先前工作进行直接比较。然后它将转向更丰富的模型，包括动态、部分观察和重叠社区。第二个主旨从算法的角度审视这些问题，并寻求设计计算高效的算法，该算法可以证明接近第一个主旨中建立的基本限制。这些算法将在合成数据集上进行验证，然后进行调整以处理真实数据集中存在的复杂性。第三个重点是将这些算法应用于在关联和消退学习实验期间从小鼠海马体收集的大规模钙成像神经数据集。目标是确定当小鼠学习任务时神经元之间的连接如何变化。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Detecting Correlated Gaussian Databases

检测相关高斯数据库

DOI：
发表时间：
2022-07
期刊：
IEEE International Symposium on Information Theory
影响因子：
0
作者：
Kahraman, Zeynep;Nazer, Bobak
通讯作者：
Nazer, Bobak

Effectively Leveraging Attributes for Visual Similarity

有效利用属性实现视觉相似性

DOI：
10.1109/iccv48922.2021.00105
发表时间：
2021-05-04
期刊：
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
影响因子：
0
作者：
Samarth Mishra;Zhongping Zhang;Yuan Shen;Ranjitha Kumar;Venkatesh Saligrama;Bryan A. Plummer
通讯作者：
Bryan A. Plummer

ActiveHedge: Hedge meets Active Learning. ICML 2022: 11694-11709

ActiveHedge：对冲与主动学习的结合。