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Activity-Dependent Tagging of Cerebellar Neurons for Studying Signal Processing and Learning

用于研究信号处理和学习的小脑神经元活动依赖性标记

基本信息

批准号：
10319181
负责人：
Jennifer L Raymond
金额：
$ 19.22万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10319181
关键词：
Architecture Area Behavior Behavior Control Behavioral Brain Calcium Calcium Signaling Cells Cerebellar Cortex Cerebellar Diseases Cerebellum Code Computer Analysis Corpus striatum structure Cytosol Disease Electrophysiology (science)Eye Movements Fiber Genes Goals Golgi Apparatus Histology Hour Image Individual Intervention Laboratories Learning Learning Skill Light Lighting Methods Modernization Modification Molecular Motor Motor Pathways Myoepithelial cell Neurons Neurosciences Opsin Outcome Pathway interactions Performance Photometry Play Property Prosencephalon Purkinje Cells Research Resistance Role Sensory Signal Pathway Signal Transduction Specificity System Techniques Vision Visual Work base cognitive function combinatorial comparative efficacy computer studies efficacy evaluation granule cell improved in vivo inhibitory neuron insight interest motor learning motor skill learning neural circuit novel strategies oculomotor oculomotor behavior optogenetics population based ratiometric recruit relating to nervous system response signal processing stellate cell temporal measurement tool vestibulo-ocular reflex visual motor visual-vestibular

项目摘要

PROJECT SUMMARY The goal of the proposed research is to implement state-of-the-art techniques for recording and manipulating neurons based on their activity in the cerebellum, to dissect the computations performed by the cerebellum to control eye movements. Vision is an active sense, and the accurate control of eye movements plays an essential role in vision. The cerebellum plays a key role in the control of eye movements, and in the refinement of eye movement accuracy and precision through oculomotor learning. It is known that the part of the cerebellum controlling eye movements receives visual and vestibular sensory information as well as copies of the eye movement commands, and presumably uses these sensory and motor signals to guide oculomotor performance and its modification by learning. However, a number of technical challenges have limited our ability to study how different sensory and motor signals are integrated in the cerebellum, and how the different signaling pathways are each modified during learning to improve oculomotor performance. Two newly developed tools, CaMPARI and Cal-Light, hold great promise to overcome some of the technical challenges that have limited studies of cerebellar computation. These tools offer advanced precision in the ability to record and manipulate neurons based on their activity during specific task conditions. We will (1) evaluate the efficacy of CAMPARI and Cal-light for selectively targeting (“tagging”), subpopulations of cerebellar neurons in a task- and activity-dependent manner, and (2) use these tools to dissect the computations implemented by the cerebellum during oculomotor performance and oculomotor skill learning. The technical outcome of the proposed work will be a new set of experimental approaches for studying the cerebellum, as well as new experimental strategies for studying computation and learning in other neural circuits. The scientific outcome will be new insights about the computations performed by the cerebellum on its sensory (visual and vestibular) and motor (efference copy) inputs. Advances in understanding how the cerebellum supports accurate eye movements will provide conceptual underpinning for developing more rational interventions for oculomotor disorders, and, more generally for the wide array of disorders associated with cerebellar dysfunction.

项目总结