Behavioral Analysis and Recording

行为分析和记录

• 批准号: 10302204
• 负责人: Paul S Katz
• 金额: $ 11.73万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10302204
• 关键词: Address; Affect; Algorithms; Animals; Behavior; Behavioral; Brain; Classification; Complex; Computational algorithm; Control Animal; Cues; Devices; Employee; Eye; Freedom; Genes; Image; Intelligence; Lesion; Machine Learning; Mechanics; Microscope; Modeling; Movement; Nerve Plexus; Nervous system structure; Neurons; Octopus; Odors; Opsin; Oral; Peripheral Nerves; Photoreceptors; Play; Process; Production; Protocols documentation; Role; Sensory; Skeleton; Stimulus; System; Tactile; Testing; Video Recording; Work; behavioral response; connectome; exoskeleton; innovation; innovative technologies; joint mobilization; machine learning algorithm; multimodality; multisensory; novel; nudibranch; olfactory stimulus; optogenetics; predicting response; relating to nervous system; response; tool; visual stimulus

PROJECT 2 SUMMARY/ABSTRACT – Project 2. Behavioral Analysis and Recording This project examines the behavior of nudibranch mollusc Berghia as part of the larger Berghia Brain Project. Careful behavioral analysis is crucial for an understanding of the neural basis of behavior. This project uses innovative approaches to understand how this animal changes its behavior in response to tactile, chemosensory, and visual stimuli. The project uses innovative technology including machine learning to automatically classify behavior, a tracking system that allows the animal to explore an arena while remaining stationary with respect to a microscope objective, and optogenetic stimulation. The questions being addressed are related to the global problems of how a brain interacts with a complex network to produce behavior. In particular, the Project addresses how multimodal sensory information affects behavioral responses.

项目2摘要/摘要 - 项目2。行为分析和记录 该项目探讨了裸体小软体动物Berghia的行为，这是较大的Berghia脑项目的一部分。 仔细的行为分析对于理解行为的神经基础至关重要。这个项目使用 创新的方法是了解该动物如何根据触觉，化学感应，化学感应，改变其行为 和视觉刺激。该项目使用创新技术，包括机器学习以自动进行分类 行为，一种跟踪系统，允许动物探索竞技场，同时保持静止 显微镜物镜和光遗传刺激。所解决的问题与全球有关 大脑如何与复杂网络相互作用以产生行为的问题。特别是该项目 解决多模式感觉信息如何影响行为响应。