CRCNS: Neural Flow Networks in Songbirds

CRCNS：鸣禽中的神经流网络

• 批准号: 7257141
• 负责人: Alexander J Hartemink
• 金额: $ 38.23万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7257141
• 关键词: Address; Affect; Algorithms; Animals; Architecture; Auditory; Basal Ganglia Diseases; Behavior; Biological; Brain; Brain region; Cognitive; Communication; Communities; Computing Methodologies; Data; Deterioration; Disease; Electroencephalography; Electrophysiology (science); Environment; Experimental Models; Functional Magnetic Resonance Imaging; Goals; Hearing; Human; Language; Learning; Mental disorders; Methods; Modeling; Monitor; Neural Pathways; Neurosciences; Pathway interactions; Process; Range; Research Personnel; Rodent; Science; Scientist; Songbirds; Stuttering; System; Testing; Time; insight; neural model; neuromechanism; nonhuman primate; relating to nervous system; sound; tool; user friendly software; vocal learning; vocalization

DESCRIPTION (provided by applicant): Determining how information flows within neural pathways is a fundamental requirement for understanding how the brain perceives, learns, and produces behavior, and how neural disorders disrupt normal cognitive processing. The goal of this proposal is to address this requirement by developing a publicly available set of inference algorithms for deciphering information flow networks in the brain, using the songbird vocal communication system as our testing and experimental model. Songbirds are one of the only accessible non-human animals where learned vocal communication, the substrate for human language, can be studied. Non-human primates, rodents, and other commonly studied animals do not have this ability. Using multielectrode array electrophysiology data from songbirds, the PIs and their colleagues have recently developed new computational methods for automatically inferring models of neural flow networks. Here, electrophysiology data will be collected from the auditory and vocal pathways of the songbird brain and provided to our new computational inference methods, in order to automatically infer the architecture of the neural flow networks that arise and evolve during auditory and vocal learning. With this tool, we can study the network mechanisms of neural deterioration of learned vocalization that occurs due to deafening, as in humans; we can study basal ganglia disorders that affect learned communication-including stuttering- thus gaining better insight into treating auditory, vocal, and mental disorders. The results will allow the PIs to formulate and experimentally test models that describe how vocal learners learn to recognize and vocalize the sounds they hear in their environment. The proposed methods will enable investigators to monitor-in near real time-how different regions of the brain exchange information during various processing and learning tasks. The proposed algorithms are applicable to a wide range of biological (and non-biological) problems. Any neural system can be studied and the methods can be applied to a range of different types of data, e.g. single or multi-unit recordings, EEG, or fMRI. To facilitate this, an entire aim of the proposal is dedicated to producing user-friendly software of high quality and modularity for general use throughout the neuroscience and general science communities. This will enable scientists to better understand how neural anatomical networks are actively utilized and how that utilization evolves over time.

描述（由申请人提供）：确定信息如何在神经通路中流动是理解大脑如何感知、学习和产生行为以及神经疾病如何破坏正常认知处理的基本要求。本提案的目标是通过开发一套公开可用的推理算法来破解大脑中的信息流网络，使用鸣禽声音通信系统作为我们的测试和实验模型来解决这一需求。鸣禽是唯一可接触的非人类动物之一，可以研究人类语言的基础——习得的声音交流。非人类灵长类动物、啮齿类动物和其他通常被研究的动物没有这种能力。利用鸣禽的多电极阵列电生理学数据，pi和他们的同事最近开发了新的计算方法来自动推断神经流网络模型。在这里，我们将从鸣禽大脑的听觉和发声路径中收集电生理学数据，并提供给我们新的计算推理方法，以自动推断在听觉和发声学习过程中产生和进化的神经流网络的结构。有了这个工具，我们可以研究耳聋导致的习得发声神经退化的网络机制，就像人类一样；我们可以研究影响习得性交流的基底神经节紊乱——包括口吃——从而更好地了解如何治疗听觉、声音和精神障碍。结果将允许pi制定和实验测试模型，描述声音学习者如何学习识别和发出他们在环境中听到的声音。所提出的方法将使研究人员能够近乎实时地监测大脑的不同区域在各种处理和学习任务中如何交换信息。所提出的算法适用于广泛的生物（和非生物）问题。任何神经系统都可以被研究，并且该方法可以应用于一系列不同类型的数据，例如单个或多单元记录，EEG或fMRI。为了促进这一点，该提案的整个目标是致力于为整个神经科学和普通科学界提供高质量和模块化的用户友好软件。这将使科学家们更好地了解神经解剖网络是如何被积极利用的，以及这种利用是如何随着时间的推移而演变的。