eMB: New Approaches for Interpreting Neural Responses to Behaviorally-Relevant Sensory Stimuli

神经信息被编码在描述神经元电脉冲时间的脉冲序列中。在神经感觉系统中，关于刺激的存在和类型的信息以这种方式编码，无论是在单个神经元水平还是在神经系统水平。神经尖峰序列已经用各种数学和统计技术研究了很多年。然而，最近出现了大量的新技术，用于分析数据集和网络，这些技术有可能应用于分析神经尖峰序列。这个项目将开发新的方法来分析神经尖峰序列使用最近发展的数学技术。该技术将用于解释大脑中负责编码味觉信息的部分记录的尖峰序列。然而，该方法可以广泛应用于其他神经感觉系统的尖峰序列。这个高度跨学科项目的团队成员包括具有化学感觉神经处理，计算神经科学和数据科学专业知识的高级研究人员。他们将在项目的各个方面与本科生和研究生一起工作。高级团队成员还参加诸如计算机编码训练营之类的研讨会，与学生和其他有兴趣学习数据分析新方法的人分享他们在数学和生物学界面上的专业知识。从神经元的脉冲序列中提取输入刺激的信息是极具挑战性的。这个项目有两个目标，描述了使用新的数学技术来实现这一目标的方法。Aim 1的重点是在单个神经元水平上，目的是确定哪些神经元编码有关刺激物的信息，以及它们如何编码这些信息。它采用贝叶斯分析，结合了最近从最优传输和突发树状图的拓扑分析中发展起来的技术。Aim 2的重点是神经集合水平，利用实验技术，允许同时记录行为动物的数十个神经元。对网络级数据的分析将建立在第一个目标中使用的技术基础上，还将使用网络科学技术，如社区检测和拓扑数据分析，以表征刺激如何在神经系统中编码。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Neural information is encoded in spike trains that describe the timing of electrical impulses in neurons. In neural sensory systems, information about the presence and type of stimulus is encoded this way, both at the level of individual neurons and at the level of neural ensembles. Neural spike trains have been studied for many years using various mathematical and statistical techniques. However, there has been a recent explosion of new techniques developed for the analysis of data sets and networks that have potential application to the analysis of neural spike trains. This project will develop new methodology for analyzing neural spike trains using recently developed mathematical techniques. The techniques will be developed for the interpretation of spike trains recorded from the part of the brain responsible for coding information about taste. However, the methodology can be broadly applied to spike trains from other neural sensory systems. The team members of this highly interdisciplinary project include senior investigators with expertise in chemosensory neural processing, computational neuroscience, and data science. They will work together with undergraduate and graduate students on all aspects of the project. Senior team members also participate in workshops such as computer coding boot camps that share their expertise at the interface of mathematics and biology with students and others interested in learning new approaches to data analysis.Extracting information about input stimuli from a neuron's spike train is extremely challenging. This project has two aims that describe approaches for doing this using new mathematical techniques. The focus of Aim 1 is at the single-neuron level, with the goal of determining which neurons code information about the stimulant, and how they code that information. It employs Bayesian analysis, in combination with techniques developed recently from optimal transport and topological analysis of burst dendrograms. The focus of Aim 2 is at the neural ensemble level, taking advantage of experimental technology that allows for the simultaneous recording from tens of neurons in the behaving animal. An analysis of this network-level data will build on techniques used in the first aim and will also use network science techniques such as community detection along with topological data analysis to characterize how stimuli are coded in neural ensembles.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.