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

eMB：解释对行为相关感官刺激的神经反应的新方法

• 批准号: 2324962
• 负责人: Richard Bertram
• 金额: $ 43.43万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2324962&HistoricalAwards=false
• 关键词: eMB; New; Approaches; Interpreting; Neural

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.

神经信息被编码在尖峰序列中，描述神经元中电脉冲的时间。在神经感觉系统中，有关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元整体水平。人们利用各种数学和统计技术对神经尖峰序列进行了多年的研究。然而，最近为分析数据集和网络而开发的新技术出现了爆炸式增长，这些技术有可能应用于神经尖峰序列的分析。该项目将开发使用最近开发的数学技术分析神经尖峰序列的新方法。这些技术将用于解释大脑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在化学感应神经处理、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该项目的各个方面合作。高级团队成员还参加计算机编码训练营等研讨会，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，描述使用新数学技术实现此目的的方法。目标 1 的重点是单神经元水平，目标是确定哪些神经元编码有关兴奋剂的信息，以及它们如何编码该信息。它采用贝叶斯分析，并结合最近从突发树状图的最佳传输和拓扑分析中开发的技术。 Aim 2 的重点是神经集合层面，利用实验技术可以同时记录行为动物的数十个神经元。对该网络级数据的分析将建立在第一个目标中使用的技术的基础上，并将使用网络科学技术，例如社区检测和拓扑数据分析来描述刺激在神经集合中的编码方式。该奖项反映了 NSF 的法定使命和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，该项目被认为值得支持。