Collaborative Research: NCS-FO: Discovering Dynamics in Massive-Scale Neural Datasets Using Machine Learning

合作研究：NCS-FO：使用机器学习发现大规模神经数据集中的动态

• 批准号: 1835390
• 负责人: Matthew Kaufman
• 金额: $ 18.9万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835390&HistoricalAwards=false
• 关键词: Collaborative; Research; NCS; FO; Discovering

For decades, neuroscientists have recorded from single brain cells (neurons) to understand how the brain senses, makes decisions, and controls movements. We can now record from hundreds of neurons simultaneously but are still at an early stage in developing tools for determining how networks of neurons work together to perceive the world and to generate the control signals needed to produce coordinated movement. Focusing on movement, this project brings to bear the power of deep learning --- powerful new machine learning algorithms --- on the problem of understanding neural activity. Because deep learning thrives on big data, the investigators can leverage massive-scale brain recordings. These include month-long recordings chronicling the activity of 100 neurons as a monkey goes about its daily business, or recording from thousands of neurons for hours in the mouse, each identified with an exact location in the brain and tied to the mouse's on-going behaviors. These approaches will open new windows on how neurons act together moment-by-moment to produce movement. The investigators will develop simple descriptions of the underlying processes to be shared with the public through venues including online tutorials, a new open course that will be developed at Emory University and Georgia Tech, the Atlanta Science Festival, and Atlanta's Brain Awareness Month. They will also make their data sets publicly available, and host data tutorial and modeling competitions at key scientific meetings, to accelerate progress by engaging the broader scientific community.In the fifty years since Ed Evarts first recorded single neurons in M1 of behaving monkeys, great effort has been devoted to understanding the relation between these individual signals and movement-related signals collected during highly constrained motor behaviors performed by over-trained monkeys. In parallel, theoreticians posited that the computations performed in the brain depend critically on network-level phenomena: dynamical laws in brain circuits that constrain the activity and dictate how it evolves over time. The goal of this project is to develop a powerful new suite of tools, based on deep learning, to analyze these dynamics at unprecedented temporal and spatial scales. The investigators will leverage recordings with month-long M1 electrophysiology, EMG, and behavioral data during natural behaviors from monkeys, and vast numbers of neurons recorded with two-photon imaging from behaving mice. Novel machine learning techniques using sequential auto-encoders will enable the investigators to learn the dynamics underlying these data. This combination will provide windows into the brain's control of motor behavior that have never before been possible. The novel analytical framework developed here will be extensible from motor behaviors to higher level problems of error processing, decision making, and learning.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.

数十年来，神经科学家从单个脑细胞（神经元）记录了大脑感官，决策和控制运动的方式。现在，我们可以同时从数百个神经元记录下来，但仍处于早期阶段，以开发用于确定神经元网络如何共同工作以感知世界并产生产生协调运动所需的控制信号的工具。该项目专注于运动，使深度学习的力量 - 强大的新机器学习算法 - 关于理解神经活动的问题。由于深度学习在大数据上蓬勃发展，因此研究人员可以利用大规模的大脑记录。 其中包括为了记录100个神经元作为猴子的活动的长达一个月的录音，或者在鼠标中从成千上万的神经元记录下来，每个神经元在鼠标中录制，每个神经元在大脑中的确切位置识别，并与鼠标的持续行为绑在一起。这些方法将为神经元如何逐步发挥作用以产生运动的新窗口。调查人员将通过包括在线教程（包括在线教程，新的开放式课程）在埃默里大学和佐治亚大学，亚特兰大科学节以及亚特兰大的大脑认识月（Atlanta的大脑宣传月）开发的场地，以及将开发新的开放课程，对公众共享的基本流程进行简单描述。他们还将在关键科学会议上公开提供数据集，并在关键的科学会议上进行托管数据教程和建模竞赛，以通过吸引更广泛的科学社区来加速进步。在Ed Evarts首次在行为猴子M1中录制了单个神经元以来的五十年来，巨大的努力一直致力于在这些单独的信​​号中的相关人士之间的关系和移动的表现良好的表现在同时进行的，并且在proving派上的表现构成了典型的运动。同时，理论家认为，大脑中执行的计算严重取决于网络级现象：脑电路中的动态定律，这些定律限制了活动并决定其随着时间的推移如何发展。该项目的目的是开发基于深度学习的强大新工具，以在前所未有的时间和空间尺度上分析这些动态。研究人员将在猴子的自然行为过程中利用长达一个月的M1电生理学，EMG和行为数据来利用记录，以及来自行为小鼠的两光子成像记录的大量神经元。使用连续自动编码器的新型机器学习技术将使研究人员能够学习这些数据的动态。这种组合将使窗户能够控制大脑对运动行为的控制，而这是前所未有的。此处开发的新分析框架将从运动行为到更高级别的错误处理，决策和学习的更高级别的问题。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估的评估来支持的。

项目成果

Representation learning for neural population activity with Neural Data Transformers

使用神经数据转换器进行神经群体活动的表示学习

• DOI: 10.51628/001c.27358
• 发表时间: 2021
• 期刊: and Theory
• 作者: Ye, Joel;Pandarinath, Chethan
• 通讯作者: Pandarinath, Chethan

Adversarial domain adaptation for stable brain-machine interfaces

• DOI: 10.48550/arxiv.1810.00045
• 发表时间: 2019-01-01
• 期刊: PREPRINT
• 作者: Farshchian, A.

Enabling hyperparameter optimization in sequential autoencoders for spiking neural data

在顺序自动编码器中针对尖峰神经数据启用超参数优化

• 发表时间: 2019
• 期刊: Advances in Neural Information Processing Systems
• 作者: Keshtkaran, MR;Pandarinath, C
• 通讯作者: Pandarinath, C

The Representation of Finger Movement and Force in Human Motor and Premotor Cortices

• DOI: 10.1523/eneuro.0063-20.2020
• 发表时间: 2020-07-01
• 期刊: ENEURO
• 影响因子: 3.4
• 作者: Flint, Robert D.;Tate, Matthew C.;Slutzky, Marc W.
• 通讯作者: Slutzky, Marc W.

From unstable input to robust output

从不稳定的输入到稳健的输出

• DOI: 10.1038/s41551-020-0587-9
• 发表时间: 2020
• 期刊: Nature Biomedical Engineering
• 影响因子: 28.1
• 作者: Wimalasena, Lahiru N.;Miller, Lee E.;Pandarinath, Chethan
• 通讯作者: Pandarinath, Chethan