Frontal/Prefrontal control of cortical rhythms during auditory active sensi

我们如何从不断变化和嘈杂的环境中提取显著信息？项目3地址 这一基本问题在知觉中使用直接大脑记录在人类（皮层电图; ECoG）评估两种感觉获得模型。主动感知模型假定， 有节奏地对感觉词进行采样并过滤掉噪音。相关预测编码模型理论 假设先前的知识增强了大脑对即将到来的刺激的预测 来强化低级的感官处理我们认为这两个过程有着相似的神经基质- 基于神经元节律的额叶、运动前区、运动和感觉皮层网络的参与， 对世界进行主动和预测性的采样，以增强感知。我们使用ECoG来测量神经元 振荡和高频活动（HG; 70-200 Hz;替代皮质内SUA活动），并采用 网络分析方法，以确定自上而下的主动传感和预测编码控制的作用 在人类大脑中。在项目4中，在猴子中进行了我们提出的两项人类ECoG研究 允许一个丰富的物种间比较的神经基板的感觉收购。AIM 1测试 假设运动/前运动系统控制听觉采样节律并主动抑制 分心的信息这一目标也探讨了外侧前额叶区域是否提供额外的控制， 运动/前运动听觉主动感知网络。AIM 2解决了先验知识如何增强 语音感知和听觉皮层中的“填充”退化语音表征。鉴于使用 本研究将仅在人类中进行。该目标直接测试预测编码 模型，并检查是否类似的神经基板支持预测编码和主动感知。AIM 3 将我们的ECoG数据与层流LFP/CSD和MUA剖面和网络参数进行了比较， 平行猴听觉项目4.这些独特的跨物种数据将用于识别细胞 在猴子和人类中产生ECoG成分的群体和生理过程 为人类皮层生理学提供了前所未有的见解。我们预测主动感知 机制是模态独立的，也将比较我们的发现，从听觉猴人， 项目1和项目2中的人类和猴子视觉主动感知研究。核心C提供关键DTI， 静息状态fMRI与我们的ECoG网络和HG数据相关，核心B提供数据 标准化和共享。最后，项目5提供了计算和建模基础设施 对建立和完善世界的细胞和系统级模型所必需的信息进行采样。主动传感和 预测编码可能在许多致残的精神、神经和发育疾病中受损。 这些疾病使得对这些过程的理解成为NIMH使命的核心。

How do we extract salient information from an ever-changing and noisy environment? Project 3 addresses this fundamental question in perception using direct brain recordings in humans (electrocorticography; ECoG) to assess two models of sensory acquisition. The Active Sensing model posits that high-level inputs act to rhythmically sample the sensory word and filter out noise. The related predictive coding model theory posits that prior knowledge enhances perception with the brain making predictions about upcoming stimuli to sharpen low-level sensory processing. We propose that both processes share similar neural substrates - neuronal rhythm-based engagement of frontal, premotor, motor and sensory cortical networks to enable active and predictive sampling of the world to enhance perception. We employ ECoG to measure neural oscillations and high frequency activity (HG; 70-200 Hz; surrogate for intracortical SUA activity) and employ network analysis approaches to define the role of top-down control of active sensing and predictive coding in the human brain. Two or our proposed human ECoG studies are performed in monkeys in Project 4 permitting a rich inter-species comparison of the neural substrates of sensory acquisition. AIM 1 tests the hypotheses that motor/premotor systems control auditory sampling rhythms and actively suppress distracting information. This aim also explores whether lateral prefrontal regions provide additional control to the motor/premotor-auditory active-sensing network. AIM 2 addresses how prior knowledge enhances speech perception and `fills-in' degraded speech representations in auditory cortices. Given the use of speech stimuli this study will only be performed in humans. This Aim directly tests the predictive coding model and examines if similar neural substrates support both predictive coding and active sensing. AIM 3 compares our ECoG data to the laminar LFP/CSD and MUA profiles and network parameters obtained in parallel monkey auditory Project 4. These unique cross-species data will be used to identify the cell populations and physiological processes that generate ECoG components in monkeys and humans providing unprecedented insights into cortical physiology in humans. We predict that active sensing mechanisms are modality independent and will also compare our finding from the auditory monkey-man to the visual human and monkey active sensing studies in Projects 1 and 2. Core C provides critical DTI and resting state fMRI to correlate with our ECoG network and HG data and Core B provides for data standardization and sharing. Finally, Project 5 provides the computational and modeling infrastructure necessary to build and refine cell and systems level models of the world is sampled. Active sensing and predictive coding are likely impaired in a host of disabling psychiatric, neurological and developmental disorders making the understanding of these processes central to the mission of the NIMH.