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Robust Network-level Inference from Neuronal Data Underlying Behavior

从行为背后的神经元数据进行稳健的网络级推理

基本信息

批准号：
2032649
负责人：
Behtash Babadi
金额：
$ 36万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032649&HistoricalAwards=false
关键词：
Robust Network level Inference Neuronal

项目摘要

Individual neurons are highly unreliable computational units in isolation, due to their drastic trial-to-trial response variability. Yet, when they act together as a network, they result in robust brain function and precise behavioral outcomes. The advent of large-scale neural recording technologies, such as two-photon calcium imaging, has created a paradigm shift by enabling scientists and engineers to study the activity of large populations of neurons in order to decipher how they collectively encode information from the external world and distill them to elicit robust behavior. In order to fully utilize these data, computationally efficient and mathematically principled techniques for robust network-level inference are required. The research objective of this proposal is to develop such methodologies to infer network-level characteristics of ensemble neuronal activity from two-photon imaging data, and to apply these methods to large-scale recordings in order to reveal the computational principles that underlie sensory processing and behavior. The research approaches include: developing a robust framework for joint inference of the intrinsic and stimulus-driven correlations of neuronal activity, designing a functional taxonomy to characterize the relevance of neuronal activity to sensory processing and behavioral outcomes, and constructing an estimation framework for capturing the dynamics and functional relevance of higher-order synchronous neuronal activity. This project addresses several outstanding challenges faced by existing methodologies, including biased network characterization incurred by two-stage analysis pipelines, intermixing the contributions of exogenous and endogenous processes to collective neuronal activity, and studying sensory processing and behavioral elicitation as disjoint problems. By employing two-photon calcium imaging data from mice and zebrafish, the proposed modeling and estimation framework will be used to investigate several fundamental problems in systems neuroscience such as tonotopic diversity in the auditory cortex, interaction of sensory processing and decision-making, and visuo-motor coordination. The project is expected to impact technology by providing signal processing solutions to be used in neural control and neuromorphic systems. The research is also integrated with educational and outreach activities including high school level workshops, undergraduate involvement in research, and course development.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.

单个神经元是孤立的高度不可靠的计算单元，这是由于它们的剧烈的试验间响应变异性。然而，当它们作为一个网络一起行动时，它们会产生强大的大脑功能和精确的行为结果。大规模神经记录技术的出现，如双光子钙成像，通过使科学家和工程师能够研究大量神经元的活动，以破译它们如何共同编码来自外部世界的信息，并提取它们以引发强大的行为，创造了一个范式转变。为了充分利用这些数据，需要计算高效和数学原理的技术来进行鲁棒的网络级推理。本提案的研究目标是开发这样的方法，从双光子成像数据推断整体神经元活动的网络级特征，并将这些方法应用于大规模记录，以揭示感觉处理和行为的计算原理。研究方法包括：开发一个强大的框架，用于联合推断神经元活动的内在和刺激驱动的相关性，设计一个功能分类来表征神经元活动与感觉处理和行为结果的相关性，并构建一个估计框架，用于捕获高阶同步神经元活动的动态和功能相关性。该项目解决了现有方法所面临的几个突出挑战，包括两阶段分析管道引起的有偏见的网络表征，混合外源性和内源性过程对集体神经元活动的贡献，以及将感觉处理和行为诱导作为不相交问题进行研究。通过采用来自小鼠和斑马鱼的双光子钙成像数据，所提出的建模和估计框架将被用于研究系统神经科学中的几个基本问题，例如听觉皮层中的tonotopic多样性，感觉处理和决策的相互作用，以及视觉-运动协调。该项目预计将通过提供用于神经控制和神经形态系统的信号处理解决方案来影响技术。该研究还与教育和推广活动相结合，包括高中水平的研讨会，本科生参与研究和课程开发。该奖项反映了NSF的法定使命，并被认为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。