To identify mechanisms of predictive processing across the distributed thalamocortical circuit

项目摘要 动物听到的许多声音都是由它们自己的行为创造出来的，并能够正确区分 这些声音对一系列行为至关重要。一个有影响力的想法是，大脑使用感觉-运动预测 预测运动产生的声音，并识别学习和执行的电路机制 这些预测对于我们理解健康和疾病中的皮质功能至关重要。因为预测性 计算包括感觉和非感觉信号的相互作用，识别潜在的电路 机制将需要了解分散但相互关联的大脑区域是如何协同工作的。而当 丘脑通常被认为是感觉信息的简单管道，二级丘脑紧密地 与感觉皮质和运动皮质相连，使其在整合感觉和非 感官信息。这一提议将检验听觉二级丘脑形状的假设 整个听觉皮质的预测性处理。首先，我将使用一种转基因小鼠品系 标记二级丘脑神经元以描绘二级听觉的精确功能联系 丘脑(目标1，K99)。接下来，我将开发一个声学增强现实家庭笼子环境，在那里老鼠 可以快速学习多种预测性行为。我将执行无线录音，而自由移动的鼠标制作 多个发音动作以确定编码的感觉、动作和预测信息 在第二级听觉丘脑(Aim 2，K99)。最后，我将执行同步多区域录制 并在行为正常的小鼠的丘脑和皮质中进行有针对性的神经干预，以确定如何预测 计算是在丘脑皮质回路(目标3，R00)上进行的。在我导师的指导下 团队，我在纽约大学制定了一个培训计划，将为我提供所需的技术技能 为了完成这些目标并对分布式电路如何将感觉和 预测加工过程中的非感官信息。拟议的培训计划还将为我提供 实现我长期职业目标的概念框架和专业技能：调查分布情况 电路机械地协同工作，使健康和疾病中的上下文相关听觉处理成为可能 一个独立的科学家。

Project Summary Many of the sounds that animals hear are created by their own actions and being able to correctly differentiate these sounds is critical to a range of behaviors. An influential idea is that the brain uses sensory-motor predictions to anticipate sounds generated by movement, and identifying the circuit mechanisms that learn and implement these predictions is critical to our understanding of cortical function in health and disease. Since predictive computations involve the interaction of sensory and non-sensory signals, identifying underlying circuit mechanisms will require understanding how distributed but interconnected brain regions work together. While the thalamus is often perceived as a simple conduit of sensory information, the second-order thalamus is tightly linked with both the sensory and motor cortex, positioning it to play a key role in integrating sensory and non- sensory information. This proposal will test the hypothesis that the auditory second-order thalamus shapes predictive processing throughout the auditory cortex. First, I will use a transgenic mouse line that specifically labels second-order thalamic neurons to map the precise functional connections of the second-order auditory thalamus (Aim 1, K99). Next, I will develop an acoustic augmented reality home cage environment where mice can rapidly learn multiple predictive behaviors. I will perform wireless recordings while freely moving mice make multiple sound-generating movements to determine the sensory, movement, and prediction information encoded in the second-order auditory thalamus (Aim 2, K99). Finally, I will perform simultaneous multi-area recordings and targeted neural interventions in the thalamus and cortex of behaving mice to determine how predictive computations are carried out across the thalamocortical circuit (Aim 3, R00). With the guidance of my mentorship team, I have developed a training plan at New York University that will provide me the technological skills needed to complete these aims and make important discoveries about how distributed circuits integrate sensory and non-sensory information during predictive processing. The proposed training plan will also provide me with the conceptual framework and professional skills to achieve my long-term career goal: to investigate how distributed circuits work together mechanistically to enable context-dependent auditory processing in health and disease as an independent scientist.