Instructive Signals for Motor Learning

 描述（由申请人提供）：神经元之间的突触是可塑的-能够变得更强或更弱。当我们学习时，新的记忆被编码在我们大脑中经过修改的突触中。这个项目的目的是了解决定哪些突触在学习过程中会发生变化的规则，以及这种变化如何导致适应性行为。特别是，我们将分析运动中的错误如何触发突触变化，从而提高后续运动的准确性。当一个不正确的动作被做出时，大脑会得到关于错误的反馈，并使用这些信息来指导在适当的突触处诱导可塑性。小脑是大脑中负责运动学习的部分，它通过一种被称为攀爬纤维的突触输入来获得错误的反馈。当运动出现错误时，攀爬纤维的活动是一个“错误信号”，它向小脑发送信息，即控制运动的电路需要通过调整某些突触的强度来调整。通常是最近活跃的突触被修改。我们将分析在攀爬纤维或其他小脑神经元的活动模式是必要的，并足以导致可塑性被诱导。控制回路中突触可塑性诱导的规则定义了回路用于学习的算法。更好地理解这些规则可以指导策略，以更有效地挖掘健康个体，神经系统疾病患者和中风后重新学习如何控制其运动的患者的神经回路的学习潜力。但是对于神经回路中的突触可塑性机制如何可以针对其控制的行为的特定计算需求而被精细调谐的更一般的理解是相关的。

 DESCRIPTION (provided by applicant): Synapses between neurons are plastic - able to become stronger or weaker. When we learn, new memories are encoded in modified synapses across our brains. The aim of this project is to understand the rules that determine which synapses will change during learning, and how that change results in an adapted behavior. In particular, we will analyze how an error in a movement acts as a trigger for synaptic change that, in turn, improves the accuracy of subsequent movements. When an incorrect movement is made, the brain gets feedback about the error, and uses this information to guide the induction of plasticity at appropriate synapses. The part of the brain responsible for motor learning, the cerebellum, gets this feedback about errors through a synaptic input known as a climbing fiber. When an error in movement occurs, activity in the climbing fiber is an "error signal" that sends the message to the cerebellum that the circuit controlling the movement needs to be adjusted by adjusting the strength of some of the synapses. It is usually the synapses that were recently active that are modified. We will analyze which patterns of activity in the climbing fibers or othe cerebellar neurons are necessary and sufficient to cause plasticity to be induced. The rules governing the induction of plasticity at the synapses in a circuit define the algorithm that circui uses to learn. A better understanding those rules can guide strategies to more effectively tap the learning potential of neural circuits in both healthy individuals, those with neurological disorder, and in patients relearning how to control their movements after stroke This proposal addresses not just unanswered questions in the field of motor learning, but is relevant for a more general understanding of how the synaptic plasticity mechanisms in a neural circuit may be finely tuned for the specific computational demands of the behavior it controls.