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

该奖项支持有关大脑网络如何使气味被检测和感知的基础研究。这样的问题在神经科学中很有意义，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人和排斥人的反应，这取决于稀释程度的微小差异，或者它们是单独遇到的，还是作为鸡尾酒的成分出现的。因此，研究大脑如何处理气味可以为动物和人类在复杂环境中如何感觉和感知提供重要线索。为了寻求这样的理解，这个项目使用了神经科学、数学和工程学的独特组合方法。在气味单独呈现和混合呈现的实验中，记录了两个不同动物物种的大脑活动。随后，数据分析和数学建模被用来识别大脑活动模式，这些模式区分动物对有问题的气味的反应。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递气味信息。为了扩大这些研究的影响，该项目包括开发一个感官神经工程暑期实习，旨在让本科生和高中生了解和体验不同的学科如何对未来的脑科学做出贡献。感官网络在多大程度上放大或抑制气味价态的感知差异，仍然是感觉神经科学中一个基本的、尚未回答的问题。这个项目的主要假设是，确实存在一套定义明确的转换，由神经动力学控制，将感觉网络活动映射到行为。具体地说，该项目将确定：(A)神经网络如何能够形成随时间变化的神经激活模式或轨迹，以响应感觉刺激；(B)从轨迹到行为结果的映射；以及(C)这种映射在不同物种之间的共性。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)和圆虫(线虫)，与计算模型和动力系统理论。神经和行为反应被记录下来，来自动物接受名义上吸引和厌恶的气味，这些数据为感觉网络和随后的行为的计算模型提供信息。这些模型对选择性或背景状态的变化可能如何调节行为反应进行预测。后者是通过一种范式进行测试的，在这种范式中，动物被系统地喂养或饥饿，从而改变了它们在厌恶-吸引光谱上的反应动态。随后，基于模型的灵敏度分析被用来预测混合响应曲线和矛盾的混合(例如，两个令人厌恶的刺激，当混合时，会引起吸引的反应)。这些预测通过以系统比率提供成分刺激来检验。因此，整个方法论将生理学实验与新的系统级分析结合在一个集成的、多学科的建模理论循环中。

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.