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

项目摘要 在整个生命过程中，人类和其他动物在声学环境中学习统计学知识并适应 他们的听力强调声音的元素，是重要的行为决定。使用这些 听力正常的人能够在拥挤嘈杂的环境中感知重要的声音， 第一次见面就能听懂对方的话。但是，周围性听力损失或 中央处理障碍通常在这些具有挑战性的环境中有听力问题，即使声音 被放大到感知阈值以上这项研究旨在描述大脑中的两个主要区域是如何 听觉网络、听觉皮层和中脑下丘，建立传入信息之间的接口， 听觉信号和内部大脑状态，选择适合当前行为的信息 上下文单个单位的神经活动将记录在清醒的雪貂在这两个大脑区域， 模拟在真实的世界中遇到的声学环境的复杂自然声音的呈现。 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。 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觉信号之间的相互作用。 先前的工作已经确定了从听觉皮层到下丘的大的离皮质投射， 在下丘产生任务依赖性的选择性变化。这项研究将测试这些作用 在下丘记录期间，通过听觉皮层的光遗传失活进行离皮质投射。 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 关于自然刺激的信息，以及这种编码如何随着注意力在特征之间的转移而变化。 在行为过程中收集的数据将用于开发模型，该模型将联合收割机自下而上的感觉处理和 自上而下的行为控制这种计算方法建立在神经网络的经典特征之上。 刺激-反应关系使用光谱-时间感受野模型。将开发新车型 将行为状态变量和非线性生物电路元素纳入已建立模型 框架。总之，这些研究将提供新的洞察力的计算策略所使用的 在现实世界中处理复杂声音的大脑。

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.