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CAREER: Deciphering Brain Function Through Dynamic Sparse Signal Processing

职业：通过动态稀疏信号处理解读大脑功能

基本信息

批准号：
1552946
负责人：
Behtash Babadi
金额：
$ 48.98万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-02-15 至 2023-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552946&HistoricalAwards=false
关键词：
CAREER Deciphering Brain Function Through

项目摘要

The ability to adapt to changes in the environment and to optimize performance against undesirable stimuli is among the hallmarks of the brain function. Capturing the adaptivity and robustness of brain function in real-time is crucial not only for deciphering its underlying mechanisms, but also for designing neural prostheses and brain-computer interface devices with adaptive and robust performance. Thanks to the advances in neural data acquisition technology, the process of data collection has been substantially facilitated, resulting in abundant pools of high-dimensional, dynamic, and complex data under various modalities and conditions from the nervous systems of animals and humans. The current modeling paradigm and estimation algorithms, however, face challenges in processing these data due to their ever-growing dimensions. This research addresses these challenges by providing a unified framework to efficiently utilize the abundant pools of data in order to deliver game-changing applications in systems neuroscience.Converging lines of evidence in theoretical and experimental neuroscience suggest that brain activity is a distributed high-dimensional spatiotemporal process emerging from sparse dynamic structures. From a computational perspective sparsity is a key ingredient in rejecting interfering signals and achieving robustness in neural computation and information representation in the brain. The main objective therefore is to develop a mathematically principled methodology that captures the dynamicity and sparsity of neural data in a scalable fashion with high accuracy. By focusing on the auditory system as a quintessential instance of sophisticated brain function, this research investigates several fundamental questions in systems neuroscience such as plasticity, attention, and stimulus decoding. The research is integrated with education and outreach activities including high school level hands-on workshops, undergraduate capstone projects, and interdisciplinary course development.

适应环境变化的能力和针对不良刺激的最佳表现是大脑功能的标志之一。实时捕捉大脑功能的自适应性和稳健性不仅对于破译其潜在的机制至关重要，而且对于设计具有自适应和稳健性能的神经假体和脑-计算机接口设备也是至关重要的。由于神经数据采集技术的进步，数据收集过程得到了极大的便利，产生了来自动物和人类神经系统的各种形式和条件下的大量高维、动态和复杂的数据池。然而，由于这些数据的维度不断增长，当前的建模范例和估计算法在处理这些数据时面临着挑战。这项研究通过提供一个统一的框架来有效地利用丰富的数据池来提供系统神经科学中改变游戏规则的应用程序来解决这些挑战。理论和实验神经科学中的证据趋同表明，大脑活动是一个从稀疏动态结构中产生的分布式高维时空过程。从计算的角度来看，稀疏性是在大脑的神经计算和信息表示中抑制干扰信号和实现稳健性的关键因素。因此，主要目标是开发一种数学上有原则的方法，以高精度、可伸缩的方式捕捉神经数据的动态性和稀疏性。通过将听觉系统作为复杂大脑功能的典型实例，本研究调查了系统神经科学中的几个基本问题，如可塑性、注意力和刺激解码。这项研究与教育和外展活动相结合，包括高中水平的动手研讨会、本科生的顶峰项目和跨学科课程开发。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Multitaper Analysis of Evolutionary Spectral Density Matrix From Multivariate Spiking Observations

多元尖峰观测的演化谱密度矩阵的多锥分析

DOI：
10.1109/dsw.2019.8755579
发表时间：
2019
期刊：
2019 IEEE Data Science Workshop (DSW
影响因子：
0
作者：
Rupasinghe, Anuththara;Babadi, Behtash
通讯作者：
Babadi, Behtash

Granger Causal Inference from Spiking Observations via Latent Variable Modeling

通过潜变量建模从尖峰观察中进行格兰杰因果推断

DOI：
10.1109/ieeeconf56349.2022.10051886
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Khosravi, Sahar;Rupasinghe, Anuththara;Babadi, Behtash
通讯作者：
Babadi, Behtash

Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO

DOI：
10.1109/tit.2023.3296336
发表时间：
2023-11-01
期刊：
IEEE TRANSACTIONS ON INFORMATION THEORY
影响因子：
2.5
作者：
Das，Proloy;Babadi，Behtash
通讯作者：
Babadi，Behtash

Granger Causal Inference from Indirect Low-Dimensional Measurements with Application to MEG Functional Connectivity Analysis

间接低维测量的格兰杰因果推断及其在 MEG 功能连接分析中的应用

DOI：
10.1109/ciss48834.2020.1570617418
发表时间：
2020
期刊：
2020 54th Annual Conference on Information Sciences and Systems (CISS
影响因子：
0
作者：
Soleimani, Behrad;Das, Proloy;Kulasingham, Joshua;Simon, Jonathan Z.;Babadi, Behtash
通讯作者：
Babadi, Behtash

Multitaper Spectral Analysis of Neuronal Spiking Activity Driven by Latent Stationary Processes