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

几十年来，神经科学家一直从单个脑细胞(神经元)进行记录，以了解大脑如何感觉、做出决定和控制运动。我们现在可以同时从数百个神经元进行记录，但仍处于开发工具的早期阶段，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。专注于运动，这个项目带来了深度学习的力量-强大的新机器学习算法-在理解神经活动的问题上。由于深度学习依靠大数据蓬勃发展，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录音，记录一只猴子日常活动中100个神经元的活动，或者记录数千个神经元在老鼠身上长达数小时的记录，每个神经元都与大脑中的确切位置有关，并与老鼠正在进行的行为有关。这些方法将为神经元如何每时每刻共同行动以产生运动打开新的窗口。研究人员将通过在线教程、埃默里大学和佐治亚理工学院开发的一门新的公开课、亚特兰大科学节和亚特兰大大脑意识月等场所，对潜在过程进行简单描述，并与公众分享。他们还将公开他们的数据集，并在关键的科学会议上主办数据教程和建模比赛，以通过参与更广泛的科学界来加快进展。自从Ed Evart首次记录到表现良好的猴子M1中的单个神经元以来，人们一直致力于了解这些个体信号与在过度训练的猴子执行高度约束运动行为时收集的运动相关信号之间的关系。同时，理论家提出，在大脑中进行的计算在很大程度上取决于网络层面的现象：大脑回路中的动力学定律限制了活动，并决定了它如何随着时间的推移而演变。这个项目的目标是开发一套基于深度学习的强大的新工具，以在前所未有的时间和空间尺度上分析这些动态。研究人员将利用猴子自然行为期间长达一个月的M1电生理学、肌电和行为数据的记录，以及行为正常小鼠的双光子成像记录的大量神经元。使用顺序自动编码器的新颖机器学习技术将使研究人员能够了解这些数据背后的动态。这一组合将提供大脑控制运动行为的窗口，这是以前从未可能实现的。这里开发的新的分析框架将从运动行为扩展到错误处理、决策和学习的更高级别的问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.