Top-down control of auditory processing in the cortico-collicular network

皮质-丘脑网络中听觉处理的自上而下控制

• 批准号: 9207441
• 负责人: Stephen V David
• 金额: $ 32.73万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9207441
• 关键词: Acoustics; Algorithms; Animals; Area; Attention; Auditory; Auditory area; Auditory system; Auricular prosthesis; Behavior; Behavior Control; Behavioral; Biological; Brain; Central Auditory Processing Disorder; Code; Complex; Computer Simulation; Crowding; Data; Detection; Devices; Disease; Elements; Environment; Feedback; Ferrets; Functional disorder; Hearing; Hearing problem; Human; Impairment; Income; Individual; Inferior Colliculus; Judgment; Learning; Life; Link; Machine Learning; Mammals; Measures; Midbrain structure; Modeling; Neurons; Noise; Non-linear Models; Pathway interactions; Patients; Peripheral; Problem Solving; Process; Research; Response to stimulus physiology; Rewards; Role; Sensory; Signal Transduction; Source; Speech; Stimulus; Stream; Structure; Synaptic plasticity; System; Testing; Time; Work; auditory processing; auditory stimulus; awake; base; behavior influence; expectation; experimental study; hearing impairment; insight; nervous system disorder; neurophysiology; novel; optogenetics; receptive field; relating to nervous system; response; selective attention; sound; tool

Project Summary Throughout life, humans and other animals learn statistical regularities in the acoustic environment and adapt their hearing to emphasize the elements of sound that are important for behavioral decisions. Using these abilities, normal-hearing humans are able to perceive important sounds in crowded noisy environments and understand the speech of individuals the first time they meet. However, patients with peripheral hearing loss or central processing disorders often have problems hearing in these challenging settings, even when sound is amplified above perceptual threshold. This study seeks to characterize how two major areas in the brain's auditory network, auditory cortex and midbrain inferior colliculus, establish an interface between incoming auditory signals and the internal brain states that select information appropriate to the current behavioral context. Single-unit neural activity will be recorded from both of these brain areas in awake ferrets during the presentation of complex naturalistic sounds that mimic the acoustic environment encountered in the real world. Internal brain state will be controlled by selective attention to specific sound features in these complex stimuli. Changes in stimulus-evoked neural activity as attention shifts among sound features will be measured to identify interactions between internal state and incoming sensory signals in these different areas. Previous work has identified a large corticofugal projection from auditory cortex to inferior colliculus that could produce task-dependent changes in selectivity in inferior colliculus. This study will test the role of these corticofugal projections by optogenetic inactivation of auditory cortex during recordings from inferior colliculus. Selective inactivation of specific pathways will characterize how the network of brain areas works together to produce effective auditory behaviors. Computational modeling tools will be used to determine, from an algorithmic perspective, how neurons encode information about the natural stimuli and how this encoding changes as attention is shifted between features. Data collected during behavior will be used to develop models that combine bottom-up sensory processing and top-down behavioral control. This computational approach builds on classic characterizations of neural stimulus-response relationships using spectro-temporal receptive field models. New models will be developed that incorporate behavioral state variables and nonlinear biological circuit elements into established model frameworks. Together, these studies will provide new insight into the computational strategies used by the behaving brain to process complex sounds in real-world contexts.

项目概要 在整个生命过程中，人类和其他动物都会学习声学环境中的统计规律并适应 他们的听力强调对行为决策很重要的声音元素。使用这些 能力，正常听力的人类能够在拥挤嘈杂的环境中感知重要的声音，并且 理解人们第一次见面时的讲话。然而，患有周围性听力损失或 中枢处理障碍通常会在这些具有挑战性的环境中出现听力问题，即使声音是 放大到感知阈值之上。这项研究旨在描述大脑中的两个主要区域如何 听觉网络、听觉皮层和中脑下丘，在传入的声音之间建立接口 听觉信号和内部大脑状态选择适合当前行为的信息 语境。在清醒雪貂的这两个大脑区域中，将记录单个单元的神经活动。 呈现复杂的自然声音，模仿现实世界中遇到的声学环境。 大脑内部状态将通过选择性注意这些复杂刺激中的特定声音特征来控制。 当注意力在声音特征之间转移时，刺激诱发的神经活动的变化将被测量 识别这些不同区域的内部状态和传入的感官信号之间的相互作用。 之前的工作已经发现从听觉皮层到下丘的一个大的皮质投射，可以 产生下丘选择性的任务依赖性变化。本研究将测试这些的作用 在下丘记录期间通过听觉皮层的光遗传学失活来进行皮质投射。 特定通路的选择性失活将表征大脑区域网络如何协同工作 产生有效的听觉行为。 计算建模工具将用于从算法的角度确定神经元如何编码 有关自然刺激的信息以及当注意力在特征之间转移时编码如何变化。 行为过程中收集的数据将用于开发结合自下而上的感官处理和 自上而下的行为控制。这种计算方法建立在神经网络的经典特征之上 使用频谱-时间感受野模型的刺激-反应关系。将开发新车型 将行为状态变量和非线性生物电路元件纳入已建立的模型中 框架。总之，这些研究将为人们所使用的计算策略提供新的见解。 大脑在现实世界中处理复杂的声音。