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.

项目摘要 在一生中，人类和其他动物在声学环境中学习统计规律并适应 他们的听力强调声音要素对于行为决策很重要。使用这些 能力，正常听觉的人能够在拥挤的嘈杂环境中感知重要的声音 第一次见面时，请了解个人的讲话。但是，外周听力损失或 中央处理障碍在这些挑战的环境中通常会听到问题，即使声音是 在上面的感知阈值上放大。这项研究旨在表征大脑中两个主要领域 听觉网络，听觉皮层和中脑较低的丘陵，建立了传入之间的接口 听觉信号和内部大脑指出，选择适合当前行为的信息 语境。在这两个大脑区域中，将在清醒雪貂中记录单单元神经活动 模仿现实世界中遇到的声学环境的复杂自然主义声音的呈现。 内部大脑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。 刺激诱发的神经活动的变化，随着声音特征之间的注意力转移将被衡量 确定内部状态与传入的感觉信号之间的相互作用。 以前的工作已经确定了从听觉皮层到下丘的大型皮质曲库投影，这可能 在下丘中产生任务依赖性的选择性变化。这项研究将测试这些作用 在下丘的记录期间，通过光遗传学灭活皮质的遗传灭活性皮质曲库投影。 特定途径的选择性灭活将表征大脑区域网络如何共同工作 产生有效的听觉行为。 计算建模工具将用于从算法的角度来确定神经元如何编码 有关自然刺激以及该编码如何随着注意力在特征之间移动而变化的信息。 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。这种计算方法以神经的经典特征为基础 使用光谱时期接受场模型的刺激反应关系。将开发新型号 将行为状态变量和非线性生物电路元素纳入建立模型 框架。这些研究将共同​​提供对有关的计算策略的新见解。 在现实世界中，表现大脑以处理复杂的声音。