CRCNS: Neural Flow Networks in Songbirds

描述（由申请人提供）：确定信息如何在神经通路内流动是理解大脑如何感知、学习和产生行为以及神经障碍如何破坏正常认知过程的基本要求。本提案的目标是通过开发一组公开可用的推理算法来破译大脑中的信息流网络，并使用鸣禽语音通信系统作为我们的测试和实验模型来解决这一要求。鸣禽是唯一一种可以研究人类语言基础的非人类动物。非人类的灵长类动物、啮齿类动物和其他经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数据，PI及其同事最近开发了新的计算方法，用于自动推断神经流网络模型。在这里，将从鸣禽大脑的听觉和发声通路收集电生理数据，并提供给我们新的计算推理方法，以便自动推断听觉和发声学习过程中出现和发展的神经流网络的结构。有了这个工具，我们可以研究由于耳聋而发生的习得性发声的神经退化的网络机制，就像在人类中一样;我们可以研究影响习得性沟通的基底神经节疾病，包括口吃，从而更好地了解治疗听觉，发声和精神疾病。研究结果将允许PI制定和实验测试模型，描述声乐学习者如何学习识别和发声，他们在他们的环境中听到的声音。所提出的方法将使研究人员能够在接近真实的时间内监测大脑的不同区域如何在各种处理和学习任务中交换信息。所提出的算法适用于广泛的生物（和非生物）问题。可以研究任何神经系统，并且可以将方法应用于一系列不同类型的数据，例如单个或多个单元记录，EEG或fMRI。为了促进这一点，该提案的整个目标是致力于生产用户友好的高质量和模块化的软件，供整个神经科学和普通科学界普遍使用。这将使科学家能够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。

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.