CRCNS Research Proposal: Collaborative Research: Wiring synaptic chain networks for precise timing during development

像唱歌和弹钢琴这样的技巧行为需要精确的时间。该项目的主要目标是使用理论和实验方法来了解神经元的网络特性，这些特性可以产生实现这些动作所需的极其精确的活动。这样的网络很可能在大脑发育过程中就已经连接起来了，但其中的确切机制仍然是个谜。先前的计算模型和实验观察表明，神经连接的过程是渐进的。该项目的研究人员将研究单个神经元是如何整合到网络中的。特别令人感兴趣的是出生后的神经元，与回路中的其他神经元相比，它们具有更多不成熟的特性，包括更高程度的自发活动，这可能有助于它们被招募到网络中。这些想法将通过实验标记和操纵未成熟的神经元，以及构建计算模型和模拟网络生长过程来验证。这些发现可能会揭示功能性神经网络是如何发展的。这项研究也可能有助于制定通过操纵神经元成熟度来修复功能失调或受伤的大脑网络的策略。这项研究将涉及广泛的创新实验和计算技术，并为学生提供获得电生理学，神经数据分析和计算神经科学现代方法的专业知识的机会。主要研究人员将培养博士后研究人员、研究生、本科生和暑期高中实习生。该项目中使用的模型系统是斑胸草雀的电机控制电路，斑胸草雀是一种鸣禽，其成年求爱歌曲由高度可重复的声音元素序列（或主题）组成，歌唱精度为毫秒。歌曲的时间是由一个叫做HVC的运动前前脑区域控制的。每个运动前HVC神经元每个基序触发一次，在不同情况下具有亚毫秒的定时抖动。作为一个群体，这些神经元驱动下游的歌曲产生回路，产生特定的声音模式。在发育过程中，当鸟儿学习唱歌时，HVC中逐渐出现了精确的定时。先前的实验观察表明，神经元逐渐被纳入网络，产生与歌曲相关的神经序列，可能来自在此期间被稳健地添加到HVC的新生神经元。本项目旨在研究HVC中序列生成网络发展的细胞和突触机制。这项工作的中心假设是，这些自发活跃的新生神经元被优先添加到生长的定时网络的前沿。这一假设将通过实验和计算建模相结合的方法进行验证：(1)通过靶向逆转录病毒方法直接成像新生神经元在体内的网络整合动态；(2)构建受这些观测约束的HVC计算模型，并利用该模型研究网络增长的机制；(3)测量新生神经元的细胞和突触特性及其成熟后的自发活动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Skilled behaviors such as singing and playing the piano require precise timing. The primary goal of this project is to use theoretical and experimental approaches to understand the network properties of neurons that can produce the extremely precise activity necessary to enable these actions. Such networks are likely to be wired during the development of the brain, but the precise mechanisms involved remain a mystery. Previous computational models and experimental observations suggest that the wiring process is gradual. The investigators of this project will study how individual neurons are incorporated into the network. Of particular interest are postnatally born neurons, which have more immature properties compared with other neurons within the circuit, including a higher degree of spontaneous activity, which potentially facilitates their recruitment into the network. These ideas will be tested by experimentally tagging and manipulating immature neurons, as well as by constructing computational models and simulating the network growth process. The findings may shed light on how functional neuronal networks develop. The research may also help to formulate strategies of repairing dysfunctional or injured brain networks through manipulation of neuron maturity. This research will involve a wide range of innovative experimental and computational techniques and provide opportunities for students to gain expertise in electrophysiology, neural data analysis, and modern methods of computational neuroscience. The principal investigators will train postdoctoral researchers as well as graduate students, undergraduates, and summer high school interns. The model system used in this project is the motor control circuitry of the zebra finch, a songbird whose adult courtship song consists of a highly repeatable sequence of vocal elements (or motif) sung with millisecond precision. The timing of song is controlled by a premotor forebrain region called HVC (proper name). Each premotor HVC neuron fires once per motif with sub-millisecond timing jitter across renditions. As a population, these neurons drive downstream song production circuits to produce specific acoustic patterns. During development, precise timing within HVC gradually emerges while the bird is learning to perform his song. Previous experimental observations suggest that neurons are gradually incorporated into the network generating song-relevant neural sequences, potentially from the newly born neurons that are robustly added to HVC during this period. This project aims to investigate the cellular and synaptic mechanisms underlying the development of the sequence generating network in HVC. The central hypothesis of this work is that these spontaneously active, newly born neurons are preferentially added to the leading edge of the growing timing network. This hypothesis will be tested with a combined experimental and computational modeling approach: (1) directly imaging the dynamics of network integration of newly born neurons in vivo through a targeted retroviral method; (2) constructing a computational model of HVC that is constrained by these observations and using the model to investigate the mechanisms of the network growth; and (3) measuring the cellular and synaptic properties of newly born neurons and their spontaneous activity as they mature.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.