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

项目摘要 在整个生命过程中，人类和其他动物学习声学环境中的统计规律并适应 他们的听力强调声音的元素，这些元素对行为决定很重要。使用这些 听力正常的人类能够在拥挤的嘈杂环境中感知重要的声音 听懂每个人第一次见面时说的话。然而，周围性听力损失的患者或 中央处理障碍在这些具有挑战性的环境中通常会有听力问题，即使声音是 放大的高于感知阈值的。这项研究试图描述大脑中的两个主要区域 听觉网络、听觉皮质和中脑下丘，在传入之间建立接口 听觉信号和选择适合当前行为的信息的内部大脑状态 背景。清醒雪貂的这两个脑区将记录到单个单位的神经活动。 呈现复杂的自然主义声音，模仿现实世界中遇到的声学环境。 大脑内部的状态将通过选择性地注意这些复杂刺激中的特定声音特征来控制。 随着声音特征之间的注意力转移，刺激诱发的神经活动的变化将被测量到 确定这些不同区域的内部状态和传入感觉信号之间的相互作用。 以前的工作已经发现了一个从听觉皮质到下丘的大型皮质分离投射，它可能 在下丘产生与任务相关的选择性改变。这项研究将测试这些因素的作用 下丘记录过程中听觉皮质的光遗传失活引起的皮质分离投射。 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。 计算建模工具将被用来从算法的角度来确定神经元如何编码 关于自然刺激的信息，以及当注意力在特征之间转移时这种编码如何变化。 在行为过程中收集的数据将被用来开发结合自下而上感觉处理和 自上而下的行为控制。这种计算方法建立在神经的经典特征之上。 使用光谱-时间感受场模型的刺激-反应关系。将开发新的型号 将行为状态变量和非线性生物电路元件合并到已建立模型中 框架。总而言之，这些研究将为我们提供新的视角，了解 让大脑在现实世界中处理复杂的声音。

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.