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

几十年来，神经科学家一直在记录单个脑细胞（神经元），以了解大脑如何感知，做出决定和控制运动。我们现在可以同时记录数百个神经元，但仍处于开发工具的早期阶段，这些工具可以确定神经元网络如何协同工作来感知世界，并产生产生协调运动所需的控制信号。该项目专注于运动，将深度学习的力量-强大的新机器学习算法-应用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，研究人员可以利用大规模的大脑记录。 其中包括长达一个月的记录，记录了猴子日常活动中100个神经元的活动，或者记录了老鼠体内数千个神经元数小时的活动，每个神经元都被识别出大脑中的确切位置，并与老鼠正在进行的行为联系在一起。这些方法将打开新的窗口，了解神经元如何时刻共同作用以产生运动。研究人员将开发简单的基本过程描述，通过包括在线教程，将在埃默里大学和格鲁吉亚理工学院开发的新开放课程，亚特兰大科学节和亚特兰大大脑意识月在内的场所与公众分享。他们还将公开他们的数据集，并在重要的科学会议上举办数据教程和建模竞赛，以通过吸引更广泛的科学界来加速进展。自艾德埃瓦茨首次记录行为猴子M1中的单个神经元以来的50年里，人们花了很大的努力来理解这些单独的信号和运动之间的关系，在过度训练的猴子进行高度限制的运动行为期间收集的相关信号。与此同时，理论家们假设，大脑中执行的计算关键取决于网络层面的现象：大脑回路中的动力学定律，这些定律限制了活动并决定了它如何随着时间的推移而演变。该项目的目标是开发一套基于深度学习的强大的新工具，以前所未有的时间和空间尺度分析这些动态。研究人员将利用猴子自然行为期间长达一个月的M1电生理学，EMG和行为数据的记录，以及行为小鼠用双光子成像记录的大量神经元。使用顺序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背后的动态。这种结合将为大脑对运动行为的控制提供前所未有的窗口。这个新颖的分析框架将从运动行为扩展到更高层次的错误处理、决策和学习问题。这个奖项反映了NSF的法定使命，并被认为是值得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来支持的。

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.