CAREER: Extracting principles of neural computation from large scale neural recordings through neural network theory and high dimensional statistics

职业：通过神经网络理论和高维统计从大规模神经记录中提取神经计算原理

基本信息

批准号：
1845166
负责人：
Surya Ganguli
金额：
$ 50万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845166&HistoricalAwards=false
关键词：
CAREER Extracting principles neural computation

项目摘要

Recent technological advances now enable recordings of thousands of neurons during complex behaviors. Such experimental capabilities could potentially reveal how the brain encodes sensations, forms memories, learns tasks, makes decisions, and generates motor actions. However, there exist major obstacles to attaining a scientific understanding of how the psychological capabilities of the mind emerge from the biological wetware of the brain. First, data analytic methods are not adequate to make sense of the massive datasets currently being gathered from the brain. Second, theoretical methods are not adequate for both optimally designing large-scale neural recordings, and bridging scales from the collective biophysics of many neurons to psychological processes underlying sensations, thoughts and actions. This project will develop novel data analytic and theoretical methods to extract a conceptual understanding of how the brain gives rise to cognition. These methods will be tested in large-scale recordings from many experimental labs studying perception, memory, learning, decision making and motor control. They will also be applied to developing better learning protocols and neural prosthetic devices.This project will pursue three overarching aims. It will build on advances in high dimensional statistics to develop a theory of when and how subsets of neurons reflect the collective dynamics of the much larger unobserved circuit in which they are embedded. This theory will provide quantitative guidance for the efficient design of future large-scale recording experiments. Second, it will build on advances in deep learning to develop algorithmic methods for extracting a conceptual understanding of how complex neural networks solve tasks. These algorithmic methods will elucidate which aspects of network connectivity and dynamics are essential to understanding how neural circuits perform their computations, thereby providing guidance for what to measure in future neuroscience experiments. Finally, it will advance theories of neural network learning to better understand how the structure of prior experience determines learned neural connectivity, and how this learning process can be optimized. These general theoretical advances will be refined and tested in specific, close experimental collaborations, involving: identifying feedback control laws in motor cortex, finding signatures of attractor dynamics in the hippocampal memory circuits, understanding the neural algorithms for perception in the retina and decision making in prefrontal cortex, and developing frameworks for understanding rapid rodent learning built upon prior experiences.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.

现在的技术进步现在可以在复杂行为期间记录成千上万个神经元。这种实验能力可能有可能揭示大脑如何编码感觉，形成记忆，学习任务，做出决策并产生运动动作。但是，从大脑的生物湿软件中出现了对思想的心理能力如何出现的科学理解的主要障碍。首先，数据分析方法不足以理解目前从大脑中收集的大规模数据集。其次，理论方法对于最佳设计大规模神经记录以及从许多神经元的集体生物物理学到感官，思想和动作的心理过程的桥接量表都不足以进行。该项目将开发新的数据分析和理论方法，以提取对大脑如何产生认知的概念理解。这些方法将在许多实验实验室的大规模记录中进行测试，这些实验实验室研究感知，记忆，学习，决策和运动控制。它们还将应用于制定更好的学习方案和神经假体设备。该项目将追求三个总体目标。它将建立在高维统计数据中的进步基础上，以发展神经元子集的何时以及如何反映其嵌入它们的较大未观察到的电路的集体动力学。该理论将为未来大规模记录实验的有效设计提供定量指导。其次，它将以深度学习的进步为基础，以开发算法方法来提取对复杂神经网络如何解决任务的概念理解。这些算法方法将阐明网络连接性和动态的哪些方面对于了解神经回路如何执行其计算至关重要，从而为未来的神经科学实验中的测量提供了指导。最后，它将推进神经网络学习的理论，以更好地了解先前经验的结构如何决定学习的神经连接性以及如何优化学习过程。这些一般理论的进步将通过特定的，紧密的实验合作进行完善和测试，涉及：确定运动皮层中的反馈控制法律，在海马记忆环路中找到吸引者动态的特征，了解在视网膜中的神经算法，了解视网膜中的感知和在培养前型群体中的经验，并在前期型号中培养框架，并在培养前期的框架中培养了框架，并在范围内进行了培训。使命，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估值得支持的。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel

DOI：
发表时间：
2020-10
期刊：
ArXiv
影响因子：
0
作者：
Stanislav Fort;G. Dziugaite;Mansheej Paul;Sepideh Kharaghani;Daniel M. Roy;S. Ganguli
通讯作者：
Stanislav Fort;G. Dziugaite;Mansheej Paul;Sepideh Kharaghani;Daniel M. Roy;S. Ganguli

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

DOI：
发表时间：
2019-06
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Niru Maheswaranathan;Alex H. Williams;Matthew D. Golub;S. Ganguli;David Sussillo
通讯作者：
Niru Maheswaranathan;Alex H. Williams;Matthew D. Golub;S. Ganguli;David Sussillo

Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED

DOI：
10.1103/physrevx.11.021048
发表时间：
2020-09
期刊：
arXiv: Quantum Physics
影响因子：
0
作者：
Brendan P. Marsh;Yudan Guo;Ronen M. Kroeze;S. Gopalakrishnan;S. Ganguli;Jonathan Keeling;B. Lev
通讯作者：
Brendan P. Marsh;Yudan Guo;Ronen M. Kroeze;S. Gopalakrishnan;S. Ganguli;Jonathan Keeling;B. Lev

Understanding self-supervised Learning Dynamics without Contrastive Pairs