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CRCNS Research Proposal: Collaborative Research: Studying Competitive Neural Network Dynamics Elicited By Attractive and Aversive Stimuli and their Mixtures

CRCNS 研究提案：合作研究：研究由吸引和厌恶刺激及其混合引起的竞争性神经网络动力学

基本信息

批准号：
1724218
负责人：
ShiNung Ching
金额：
$ 46.95万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724218&HistoricalAwards=false
关键词：
CRCNS Research Proposal Collaborative Studying

项目摘要

This award supports basic research regarding the question of how networks in the brain allow odors to be detected and perceived. Such a question is of fundamental interest in neuroscience because responding to odors or scents is one of the most basic ecological abilities exhibited across different animal species. Further, responses to odors are highly dependent on context. For example, certain smells may create both attractive and repulsive reactions, depending on small differences in dilution or whether they are encountered alone or as components in a cocktail. Thus, studying how the brain processes odors can provide important clues regarding how animals and humans sense and perceive in complex environments. In seeking such understanding, this project uses a unique combination of methods from neuroscience, mathematics, and engineering. Brain activity from two different animal species are recorded during experiments in which odors are presented in isolation and in mixtures. Subsequently, data analysis and mathematical modeling is used to identify brain activity patterns that distinguish the reaction of the animals to the odors in question. Hence, the project uncovers how particular brain networks transform and transmit odor information in a way that is central to the sense of smell. To broaden the impact of these studies, the project includes the development of a summer internship in sensory neural engineering, intended to allow undergraduate and high school students to learn about and experience how different academic disciplines contribute to future brain science.The extent to which sensory networks amplify or suppress perceived differences in odor valence remains a fundamental, unanswered question in sensory neuroscience. The overarching hypothesis of this project is that indeed, there exists a well-defined set of transformations, governed by neuronal dynamics, which map sensory network activity to behavior. Specifically, the project will determine: (a) How neural networks enable the formation of time-varying neural activation patterns, or, trajectories, in response to sensory stimuli, (b) The mapping from trajectories to behavioral outcome, and (c) The commonality of this mapping across species. The research goals use an interdisciplinary approach combining sensory systems neuroscience in two species, locusts (Schistocerca americana) and round worms (C. elegans), with computational modeling and dynamical systems theory. Neural and behavioral responses are recorded from animals receiving nominally attractive and aversive odors, and these data inform computational models of the sensory networks and ensuing behaviors. The models generate predictions on how behavioral responses might be modulated by a change in selectivity, or background state. The latter is tested through a paradigm wherein animals are systematically fed or starved, thus shifting their response dynamics on the aversive-attractive spectrum. Subsequently, model-based sensitivity analyses is used to predict mixture response curves and paradoxical mixtures (e.g., two aversive stimuli that when mixed, elicit an attractive response). These predictions are tested by delivering component stimuli in systematic ratios. Thus, the overall methodology combines physiological experiments with new systems-level analysis in an integrated, multidisciplinary modeling-theory loop.

该奖项支持关于大脑网络如何允许气味被检测和感知的基础研究。这样的问题是神经科学的根本兴趣，因为对气味或气味的反应是不同动物物种表现出的最基本的生态能力之一。此外，对气味的反应高度依赖于环境。例如，某些气味可能会产生吸引和排斥反应，这取决于稀释的微小差异，或者它们是单独遇到的还是作为鸡尾酒中的成分。因此，研究大脑如何处理气味可以提供有关动物和人类如何在复杂环境中感知和感知的重要线索。在寻求这种理解的过程中，该项目使用了神经科学，数学和工程学方法的独特组合。两种不同动物的大脑活动在实验中被记录下来，在实验中，气味被单独和混合。随后，数据分析和数学建模被用来识别大脑活动模式，以区分动物对所讨论的气味的反应。因此，该项目揭示了特定的大脑网络如何以一种对嗅觉至关重要的方式转换和传输气味信息。为了扩大这些研究的影响，该项目包括在感觉神经工程暑期实习的发展，旨在让本科生和高中生学习和体验不同的学科如何为未来的脑科学做出贡献。感觉网络在多大程度上放大或抑制气味效价的感知差异仍然是感觉神经科学中一个基本的，未回答的问题。这个项目的首要假设是，确实存在一组定义明确的转换，由神经元动力学控制，将感觉网络活动映射到行为。具体来说，该项目将确定：（a）神经网络如何使时变神经激活模式的形成，或，轨迹，响应感官刺激，（B）从轨迹到行为结果的映射，以及（c）这种映射跨物种的共性。研究目标使用跨学科的方法，结合两个物种的感觉系统神经科学，蝗虫（美洲血吸虫）和蠕虫（C。elegans），与计算建模和动力系统理论。神经和行为反应记录从动物接收名义上有吸引力的和厌恶的气味，这些数据通知的感觉网络和随后的行为的计算模型。这些模型生成关于行为反应如何通过选择性或背景状态的变化来调节的预测。后者通过一个范例进行测试，其中动物系统地喂养或饥饿，从而改变他们的反应动力学的厌恶吸引力的频谱。随后，基于模型的灵敏度分析用于预测混合物响应曲线和矛盾混合物（例如，两个厌恶的刺激，当混合时，引起有吸引力的反应）。这些预测进行测试，提供组件刺激系统的比例。因此，整体方法结合了生理实验与新的系统级分析，在一个综合的，多学科的建模理论循环。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Neural Circuit Dynamics for Sensory Detection

DOI：
10.1523/jneurosci.2185-19.2020
发表时间：
2020-01
期刊：
The Journal of Neuroscience
影响因子：
0
作者：
Sruti Mallik;Srinath Nizampatnam;Anirban Nandi;D. Saha;B. Raman;ShiNung Ching
通讯作者：
Sruti Mallik;Srinath Nizampatnam;Anirban Nandi;D. Saha;B. Raman;ShiNung Ching

Top-down modeling of distributed neural dynamics for motion control

用于运动控制的分布式神经动力学的自上而下建模