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

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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.