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.

神经信息在描述神经元中电脉冲的时机的尖峰列表中编码。在神经感觉系统中，有关刺激的存在和类型的信息都是在单个神经元和神经集合级别的水平上编码的。使用各种数学和统计技术，已经研究了多年的神经尖峰火车。但是，最近开发了针对数据集和网络分析的新技术的爆炸，这些数据集和网络潜在地应用于神经尖峰火车的分析。该项目将开发新的方法，用于使用最近开发的数学技术来分析神经尖峰火车。这些技术将开发用于解释从负责编码口味信息的大脑中记录的尖峰火车。但是，该方法可以广泛应用于其他神经感觉系统的尖峰列车。这个高度跨学科项目的团队成员包括具有化学感应神经处理，计算神经科学和数据科学专业知识的高级研究人员。他们将与本科生和研究生一起在项目的各个方面合作。高级团队成员还参加了诸如计算机编码训练营之类的研讨会，这些训练营在数学和生物学的界面上与学生以及有兴趣学习新方法进行数据分析的其他人分享他们的专业知识。从神经元的尖峰火车上提取有关输入刺激的信息非常具有挑战性。该项目有两个目的，描述了使用新的数学技术来实现此目的的方法。 AIM 1的重点是在单神经元级别上，目的是确定哪些神经元代码有关兴奋剂的信息以及它们如何编码该信息。它采用了贝叶斯分析，结合了最近从最佳运输和爆发树状图的拓扑分析开发的技术。 AIM 2的重点是在神经集成水平上，利用实验技术，可以同时记录行为动物中数十个神经元的记录。对该网络级别数据的分析将基于第一个目标中使用的技术，还将使用网络科学技术，例如社区检测以及拓扑数据分析来表征神经合奏中的刺激是如何编码的。该奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛影响来评估NSF的法定任务，并被认为是值得的支持。