Principles of operation of a neural learning circuit

神经学习电路的工作原理

• 批准号: 9975424
• 负责人: David James Herzfeld
• 金额: $ 10.65万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9975424
• 关键词: Adaptive Behaviors; Affect; Animals; Architecture; Award; Behavior; Behavior Control; Behavioral; Brain; Cells; Cerebellar Cortex; Cerebellar vermis structure; Cerebellum; Characteristics; Complex; Computer Models; Coupled; Data; Electrodes; Elements; Event; Eye; Face; Fiber; Frequencies; Goals; Individual; Language; Lead; Learning; Link; Mediating; Membrane; Memory; Mentors; Modality; Modeling; Modification; Motor; Movement; Nature; Neuroanatomy; Neurons; Output; Pattern; Phase; Play; Process; Property; Purkinje Cells; Recurrence; Research; Rewards; Role; Saccades; Signal Transduction; Site; Speed; Structure; Synapses; Synaptic plasticity; System; Testing; Time; base; behavior observation; experience; infancy; insight; learned behavior; motor behavior; motor learning; neurophysiology; oculomotor; operation; relating to nervous system; response; sensory stimulus; statistics; theories

PROJECT SUMMARY We can learn and successfully recall faces, events, language, concepts, places, facts, things that were frightening or rewarding, and the movement commands required to skillfully move our motor effectors. Decades of scientific research have pointed to the role of synaptic plasticity as the basic currency of the brain’s ability to learn and remember. While we have a fairly detailed understanding of the rules that govern changes in synaptic strength and modifications of a neuron’s intrinsic membrane excitability, our understanding of how these plastic changes lead to behavioral learning and memory is still in its infancy. Behavioral learning is an emergent property of a complete neural learning circuit in which the sites and mechanisms of plasticity are embedded. Without an understanding of the effects of plasticity at the circuit-level, we cannot truly understand learning and memory. Arguably, motor adaptation is the domain where we have best chance to understand the circuit-level rules that govern learning, due to the exquisite relationship between sensory stimuli and adaptive behavior. The cerebellum has been shown to be the brain structure crucial for motor learning, and provides a neural locus to begin to outline the circuit rules that govern learning. Our goal is to leverage the highly conserved cytoarchitecture of the cerebellar circuit to identify the principles of operation that underpin neural learning circuits more generally. During the mentored phase of this award, we will focus on a well-described cerebellar-dependent behavior: pursuit direction learning. Even after the occurrence of a single movement error in this task, the brain learns from the mistake, attempting to minimize the error in the next trial. In the first aim, we will characterize the signals that drive the error-dependent acquisition of this motor memory at a specific synapse in the cerebellar circuit, which is thought to contribute to the bulk of behavioral motor learning. During the second aim, we will record from the complete cerebellar circuit. Our goal is to describe how individual elements and synapses in the cerebellar circuit contribute to behavioral adaptation, including constraints on the site(s) of plasticity that cause behavioral learning and allowing conclusions about the extent to which learning occurs before, inside, or downstream of the cerebellar cortex. During the independent phase, we will again record from the complete cerebellar circuit during a different cerebellar learning task: saccadic adaptation. Using an adaptive behavior that relies on a different cerebellar region, we can begin to dissect the circuit-level principles that generalize broadly across cerebellar learning. Together, our results will provide the first circuit-level rules that underlie behavioral learning. These results should have broad implications across other learning and memory systems, all of which exist as complex circuits that drive behavior.

项目概要 我们可以学习并成功回忆起面孔、事件、语言、概念、地点、事实、事物 令人恐惧或有益，以及熟练地移动我们的运动效应器所需的运动命令。 数十年的科学研究已经指出突触可塑性作为大脑的基本货币的作用 学习和记忆的能力。虽然我们对管理变革的规则有相当详细的了解 在突触强度和神经元内在膜兴奋性的改变中，我们对如何理解 这些可塑性变化导致行为学习和记忆仍处于起步阶段。行为学习是一种 完整神经学习回路的突现属性，其中可塑性的位点和机制是 嵌入的。如果不了解可塑性在电路层面的影响，我们就无法真正理解 学习和记忆。可以说，运动适应是我们最有机会理解的领域 由于感觉刺激和适应性之间的微妙关系，控制学习的电路级规则 行为。小脑已被证明是对运动学习至关重要的大脑结构，并提供了 神经轨迹开始概述控制学习的电路规则。我们的目标是利用高度 小脑回路的保守细胞结构以确定支撑神经的运作原理 更一般地学习电路。在该奖项的指导阶段，我们将重点关注一个详细描述的 小脑依赖行为：追求方向学习。即使发生单一动作之后 如果在此任务中出现错误，大脑会从错误中学习，尝试在下一次尝试中将错误最小化。在第一个 目标是，我们将表征驱动该运动存储器的误差相关采集的信号 小脑回路中的特定突触被认为有助于大部分行为运动学习。 在第二个目标期间，我们将从完整的小脑回路进行记录。我们的目标是描述如何 小脑回路中的各个元件和突触有助于行为适应，包括 对导致行为学习的可塑性位点的限制并允许得出有关程度的结论 学习发生在小脑皮层之前、内部或下游。在独立阶段， 我们将在不同的小脑学习任务期间再次记录完整的小脑回路：扫视 适应。使用依赖于不同小脑区域的适应性行为，我们可以开始剖析 广泛推广到小脑学习的电路级原理。我们的成果将共同提供 作为行为学习基础的第一个电路级规则。这些结果应该具有广泛的影响 其他学习和记忆系统，所有这些系统都作为驱动行为的复杂电路而存在。