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

熟练的行为，如唱歌和弹钢琴，需要精确的时间。这个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属性，这些属性可以产生实现这些行动所需的极其精确的活动。这样的网络很可能是在大脑发育过程中连接起来的，但其中涉及的确切机制仍然是一个谜。以前的计算模型和实验观测表明，布线过程是渐进的。该项目的研究人员将研究单个神经元是如何融入网络的。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神经元，以及通过构建计算模型和模拟网络生长过程来检验。这一发现可能有助于阐明功能性神经元网络是如何发展的。这项研究还可能有助于制定通过操纵神经元成熟来修复功能失调或受损的大脑网络的策略。这项研究将涉及广泛的创新实验和计算技术，并为学生提供在电生理学、神经数据分析和计算神经科学的现代方法方面获得专业知识的机会。主要研究人员将培训博士后研究人员以及研究生、本科生和暑期高中实习生。这个项目中使用的模型系统是斑马雀的电机控制电路，斑马雀是一种鸣禽，它的成年求爱歌曲由一系列高度可重复的声音元素(或主题)组成，以毫秒的精度演唱。歌唱的时间由一个叫做HVC(专有名称)的运动前前脑区域控制。每个运动前HVC神经元在每个基序上都会触发一次，在不同的重现中会有亚毫秒的计时抖动。作为一个群体，这些神经元驱动下游的歌曲产生电路来产生特定的声学模式。在发育过程中，当这只鸟学习表演它的歌声时，HVC内的精确计时逐渐出现。先前的实验观察表明，神经元逐渐被整合到网络中，产生与歌曲相关的神经序列，潜在地来自于在这一时期强劲地添加到HVC中的新生神经元。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优先添加到不断增长的计时网络的前沿。这一假设将通过实验和计算相结合的建模方法得到验证：(1)通过有针对性的逆转录病毒方法直接成像体内新生神经元网络整合的动力学；(2)构建受这些观察约束的HVC计算模型，并使用该模型来研究网络增长的机制；以及(3)测量新生神经元的细胞和突触属性以及它们在成熟时的自发活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.