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

神经信息被编码在尖峰序列中，尖峰序列描述了神经元中电脉冲的时间。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的水平上还是在神经集合的水平上。多年来，人们已经使用各种数学和统计技术对神经尖峰序列进行了研究。然而，最近有一个爆炸性的新技术开发的数据集和网络的分析，有潜在的应用程序的神经尖峰序列的分析。本计画将利用最新发展的数学技术，发展分析神经尖峰序列的新方法。这些技术将被开发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，该方法可以广泛地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在化学感觉神经处理，计算神经科学和数据科学方面具有专业知识的高级研究人员。他们将与本科生和研究生在项目的各个方面一起工作。高级团队成员还参加计算机编码靴子训练营等研讨会，与学生和其他对学习数据分析新方法感兴趣的人分享他们在数学和生物学接口方面的专业知识。从神经元的尖峰序列中提取有关输入刺激的信息极具挑战性。该项目有两个目标，描述了使用新的数学技术来实现这一目标的方法。Aim 1的重点是在单神经元水平上，目的是确定哪些神经元编码有关刺激的信息，以及它们如何编码这些信息。它采用贝叶斯分析，结合最近开发的技术，从最佳运输和拓扑分析的爆发树状图。目标2的重点是在神经系综水平，利用实验技术，允许同时记录行为动物中的数十个神经元。这个网络级数据的分析将建立在第一个目标中使用的技术，也将使用网络科学技术，如社区检测沿着拓扑数据分析，以表征刺激是如何编码在神经ensembles.This奖项反映了NSF的法定使命，并已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的支持。

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.