CRCNS: Neural Flow Networks in Songbirds

CRCNS：鸣禽中的神经流网络

• 批准号: 7647915
• 负责人: Alexander J Hartemink
• 金额: $ 39.96万
• 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 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7647915
• 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; 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）：确定信息在神经通路中的流动如何是了解大脑如何感知，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。该提案的目的是通过使用鸣禽声音通信系统作为我们的测试和实验模型来开发一套公开可用的推理算法来解决这一要求。鸣禽是唯一可以研究唯一可访问的非人类动物的动物之一，可以研究学习的声带交流，即人类语言的基材。非人类灵长类动物，啮齿动物和其他常见的动物没有这种能力。 PIS及其同事使用来自鸣禽的多电极阵列电生理学数据，最近开发了新的计算方法，以自动推断神经流动网络的模型。在这里，电生理数据将从鸣鸟大脑的听觉和声音途径中收集，并提供给我们新的计算推理方法，以自动推断出在听觉和声音学习过程中出现和演变的神经流网络的结构。借助此工具，我们可以研究由于人类的震耳欲聋而发生的学习发声的神经恶化的网络机制。我们可以研究影响学习沟通的基础神经节疾病，包括口吃 - 从而更好地洞悉治疗听觉，人声和精神障碍。结果将使PI可以制定和实验测试模型，以描述声乐学习者如何学会识别和发声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在各种处理和学习任务中监视大脑交换信息的不同区域。所提出的算法适用于广泛的生物学（和非生物）问题。任何神经系统都可以研究，这些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，脑电图或fMRI。为了促进这一点，该提案的全部目的致力于生产具有高质量和模块化的用户友好软件，以供整个神经科学和一般科学社区中使用。这将使科学家能够更好地了解神经解剖网络如何积极利用以及该利用方式随着时间的流逝如何发展。

项目成果

Different mechanisms are responsible for dishabituation of electrophysiological auditory responses to a change in acoustic identity than to a change in stimulus location.

• DOI: 10.1016/j.nlm.2013.08.010
• 发表时间: 2013-11
• 期刊: NEUROBIOLOGY OF LEARNING AND MEMORY
• 影响因子: 2.7
• 作者: Smulders, Tom V.;Jarvis, Erich D.
• 通讯作者: Jarvis, Erich D.

Computational inference of neural information flow networks.

• DOI: 10.1371/journal.pcbi.0020161
• 发表时间: 2006-11-24
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Smith, V. Anne;Yu, Jing;Smulders, Tom V.;Hartemink, Alexander J.;Jarvis, Erich D.
• 通讯作者: Jarvis, Erich D.