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Single neuron mechanisms of sensory-motor learning

感觉运动学习的单神经元机制

基本信息

批准号：
8927703
负责人：
Timothy James Gardner
金额：
$ 35.81万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8927703
关键词：
Address Adult Animal Behavior Area Basal Ganglia Behavior Birds Brain Cells Code Data Disease Fire - disasters Future Health Hour Human Individual Injury Learning Length Life Mammals Martes zibellina Measurement Measures Memory Microscopic Monitor Motor Motor Cortex Motor Skills Movement Nature Neurons Noise Patients Pattern Performance Plant Roots Process Property Prosthesis Published Comment Recording of previous events Recovery Relative (related person)Research Resolution Rewards Sensory Shapes Signal Transduction Songbirds Testing Therapeutic Therapeutic Intervention Time Trauma base bird song brain computer interface brain machine interface calcium indicator design improved insight learned behavior millisecond motor control motor learning motor skill learning nerve injury neural circuit programs relating to nervous system research study sensory feedback sensory mechanism tool zebra finch

项目摘要

DESCRIPTION (provided by applicant): Humans maintain learned motor skills over long time-scales-for days, years or even decades. However, little is known about how the brain achieves this stability. Some studies indicate that while motor skills can remain stable for years, the individual neurons controlling them may significantly change their firing properties over the course of hours. In another view, the tuning of individual neurons is as stable as the motor skill itself. The central hypothesis of this project is that the brain encodes learned behaviors on two distinct levels - a mesoscopic level that is highly stable, and a microscopic level in which single neurons change and are influenced by the recent history of motor performance errors. In other words, the stability of a memory is rooted not in single neuron stability, but in network patterns that persist in spite of drifting activity in individual neurons. This project investigates this hypothesis by examining the neural basis of song in zebra finches. The neural circuits that underly song behavior are well defined, extensively studied, and in key respects homologous to the cortico-basal ganglia circuits that underly sensory-motor learning in mammals. For this project, the key value of the songbird is the stability of its behavior. A songbird can sing the same learned song with great precision for years providing a unique opportunity to examine how motor skills are preserved over long time-scales. Using new tools for stable recording from neurons, the project examines single neuron tuning and network patterns underlying song over time scales of days to months. To accelerate changes in the song motor program the project uses a brain-machine interface that generates brief bursts of noise during singing whenever the brain activates specific groups of neurons. Preliminary data reveals that birds can learn to reduce this interfering noise, and improve the quality of their songs by controlling the pattern of activity in the targeted neurons. Through the brain-machine interface and other experiments, significant preliminary data reveals that whereas mesoscopic dynamical patterns in premotor cortex are stable, individual neurons can drift in and out of the ensemble pattern, and adjust their activity to minimize performance errors. This project will reveal the rules of this process with cellular resolution. Insights gained from these experiments have the potential to impact human health. If single neurons drift in motor control, then knowing the rules that govern this drift will be critical to therapeutic interventions that promote recovery after injury, or create sable brain- machine interfaces for human prosthetics.

描述（由申请人提供）：人类在很长一段时间内保持习得的运动技能——几天、几年甚至几十年。然而，人们对大脑如何实现这种稳定性知之甚少。一些研究表明，虽然运动技能可以保持稳定数年，但控制它们的单个神经元可能会在数小时内显著改变其放电特性。另一种观点认为，单个神经元的调节和运动技能本身一样稳定。这个项目的核心假设是，大脑在两个不同的层面上对习得行为进行编码——一个是高度稳定的中观层面，另一个是单一的微观层面