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NeuroNex Theory Team: Inferring interactions between neurons, stimuli, and behavior

NeuroNex 理论团队：推断神经元、刺激和行为之间的相互作用

基本信息

批准号：
1707400
负责人：
Kresimir Josic
金额：
$ 439.32万
依托单位：
University of Houston
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-12-01 至 2024-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707400&HistoricalAwards=false
关键词：
NeuroNex Theory Team Inferring interactions

项目摘要

Scientists have learned much about how the activity of individual neurons relates to sensations, thoughts, feelings, and behavior. However, all neurons live and function within the enormous web that is the brain. Scientists are still far from understanding how the complex, dynamic dance between the cells that form this web makes us who we are. While new technologies allow scientists to observe the activity of massive numbers of neurons in living brains, they still cannot make sense of such data. The goal of this NeuroNex Theory team is to develop a new lens - a set of mathematical and computational tools - for making sense of the dynamic brain activity data. These techniques will allow them to deduce when and how observed neurons interact, how these interactions are altered by stimuli, and how they may govern behavior. The resulting combination of revolutionary experimental and mathematical tools will provide neuroscientists with unprecedented insights into the brain's distributed neural computations.This NeuroNex Theory team will develop statistical tools that will provide insights into the computations performed by neuronal ensembles, by relating what the animal observes to recorded neural activity as well as how the neural activity relates to behavior. They will validate the methods using simulated neuronal networks, and apply them to recordings from the mouse brain. The team will also develop and test their approach using modern deep neural networks (convolutional nets and recurrent architectures) that achieve or exceed human performance in many hard tasks. This will allow them to quantify how interactions in these artificial networks change with the stimulus, and compare and contrast the results with data from animals. This is a broad set of goals that requires a combination of approaches and diverse expertise. The team therefore consists of experimental and theoretical neuroscientists, mathematicians, and statisticians who will work together closely. The true test of this work will be the impact it will have on brain science in general. The team is therefore committed to sharing their techniques, code and data. They have also assembled a group of End Users to be early adopters and testers of these methods. This NeuroNex Theory Team award is funded as part of the BRAIN Initiative and NSF's Understanding the Brain activities.

科学家们已经了解了很多关于单个神经元的活动如何与感觉，思想，情感和行为相关的知识。然而，所有的神经元都在大脑这个巨大的网络中生活和发挥作用。科学家们仍然远未理解形成这个网络的细胞之间复杂而动态的舞蹈如何使我们成为我们自己。虽然新技术使科学家能够观察活体大脑中大量神经元的活动，但他们仍然无法理解这些数据。这个NeuroNex理论团队的目标是开发一种新的透镜-一套数学和计算工具-用于理解动态大脑活动数据。这些技术将使他们能够推断出观察到的神经元何时以及如何相互作用，这些相互作用如何被刺激改变，以及它们如何控制行为。革命性的实验和数学工具的结合将为神经科学家提供对大脑分布式神经计算的前所未有的见解。这个NeuroNex理论团队将开发统计工具，通过将动物观察到的与记录的神经活动以及神经活动如何与行为联系起来，提供对神经元集合执行的计算的见解。他们将使用模拟的神经元网络验证这些方法，并将其应用于小鼠大脑的记录。该团队还将使用现代深度神经网络（卷积网络和递归架构）开发和测试他们的方法，这些网络在许多硬任务中达到或超过人类性能。这将使他们能够量化这些人工网络中的相互作用如何随着刺激而变化，并将结果与动物数据进行比较和对比。这是一系列广泛的目标，需要结合各种方法和不同的专业知识。因此，该团队由实验和理论神经科学家，数学家和统计学家组成，他们将密切合作。这项工作的真正考验将是它对整个脑科学的影响。因此，该团队致力于分享他们的技术，代码和数据。他们还召集了一批最终用户，作为这些方法的早期采用者和测试者。这个NeuroNex理论团队奖是作为大脑倡议和NSF的理解大脑活动的一部分资助的。

项目成果

期刊论文数量（39）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Inference of synaptic connectivity and external variability in neural microcircuits

DOI：
10.1007/s10827-020-00739-4
发表时间：
2020-02-21
期刊：
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
影响因子：
1.2
作者：
Baker, Cody;Froudarakis, Emmanouil;Rosenbaum, Robert
通讯作者：
Rosenbaum, Robert

Supervised convex clustering

DOI：
10.1111/biom.13860
发表时间：
2020-05
期刊：
Biometrics
影响因子：
1.9
作者：
Minjie Wang;Tianyi Yao;Genevera I. Allen
通讯作者：
Minjie Wang;Tianyi Yao;Genevera I. Allen

Thresholded graphical lasso adjusts for latent variables

阈值图形套索调整潜在变量

DOI：
10.1093/biomet/asac060
发表时间：
2022
期刊：
Biometrika
影响因子：
2.7
作者：
Wang, Minjie;Allen, Genevera I
通讯作者：
Allen, Genevera I

Hierarchical Modular Structure of the Drosophila Connectome

果蝇连接组的分层模块化结构

DOI：
10.1523/jneurosci.0134-23.2023
发表时间：
2023
期刊：
The Journal of Neuroscience
影响因子：
0
作者：
Kunin, Alexander B.;Guo, Jiahao;Bassler, Kevin E.;Pitkow, Xaq;Josić, Krešimir
通讯作者：
Josić, Krešimir

Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo

DOI：
10.1038/s41593-023-01446-w
发表时间：
2023-10
期刊：
Nature Neuroscience
影响因子：
25
作者：
Ariana R. Andrei;A. E. Akil;Natasha Kharas;R. Rosenbaum;K. Josić;Valentin Dragoi
通讯作者：
Ariana R. Andrei;A. E. Akil;Natasha Kharas;R. Rosenbaum;K. Josić;Valentin Dragoi