权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Building a Complete, Predictive, Data-Driven Model of Action Selection During Olfactory Navigation

在嗅觉导航过程中建立完整的、预测性的、数据驱动的动作选择模型

基本信息

批准号：
10225530
负责人：
MATTHIEU R. P. J. C. G. LOUIS
金额：
$ 38.88万
依托单位：
UNIVERSITY OF CALIFORNIA SANTA BARBARA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10225530
关键词：
Anatomy Animal Model Animals Architecture Behavior Behavioral Brain Cell model Chemotaxis Computer Models Cues Data Decision Making Detection Diffusion Drosophila melanogaster Electrons Electrophysiology (science)Elements Environment Esthesia Food Goals Heart Individual Larva Maps Measures Methods Microfluidics Modality Modeling Motor Movement Nervous system structure Neurons Noise Odors Olfactory Pathways Organism Output Partner in relationship Pathway interactions Pattern Peripheral Play Problem Solving Process Resolution Role Running Sensory Series Signal Transduction Smell Perception Source Speed Stimulus System Systems Theory Testing Time Toxin Validation Work base behavioral response behavioral tolerance dynamic system experimental study fly improved light microscopy neural circuit neural model neuroimaging neuromechanism olfactory sensory neurons olfactory stimulus optogenetics predictive modeling relating to nervous system response sensory input sound theories tool virtual virtual reality

项目摘要

Abstract To survive, living organisms must collect information about their environment and use it to select appropriate behaviors. However, information from the environment is often noisy, incomplete and ambiguous. Currently, no theory or model comprehensively explains how nervous systems solve the problem of navigation based on noisy information. Without such a theory, we cannot improve the ability of living systems or autonomous machines to make better decisions by processing the imperfect sensory information that is typically available to them. We propose to build a complete data-driven model of how nervous systems turn noisy sensory information into action selection during navigation. We have previously been able to decipher aspects of this process by studying the Drosophila melanogaster larva — a small, transparent organism that is exceptionally good at navigating towards food odors despite having only 10,000 neurons. My lab has developed methods to rigorously quantify odor landscapes; measure how neurons represent these odors; automatically track larval movement; create virtual sensory realities for the larva; and change the real-time behavior of the larva on- demand with optogenetics. We have also recently mapped an entire pathway within the larval nervous system. Here, we will determine how and when noisy sensory information causes the larva to reorient (stop and turn) as it is navigating towards an attractive odor source (chemotaxis). Our objective is to uncover the neural mechanisms that accumulate, filter, and process noisy sensory evidence and use ambiguous information to make coherent perceptual decisions (action selection). By combining theory, experiments, and modeling, we will iteratively build a quantitative model which predicts the cellular and circuit-level computations transforming sensory (olfactory) signals into navigational decision-making (chemotaxis) that is robust to environmental disturbances (noise).

摘要