To identify mechanisms of predictive processing across the distributed thalamocortical circuit

项目概要 动物听到的许多声音都是由它们自己的行为产生的，并且能够正确区分 这些声音对于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，并识别学习和实施的电路机制 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。既然预测 计算涉及感觉和非感觉信号的相互作用，识别底层电路 机制需要了解分布式但相互关联的大脑区域如何协同工作。同时 丘脑通常被认为是感觉信息的简单管道，二阶丘脑紧密相连 与感觉皮层和运动皮层相连，使其在整合感觉和非感觉皮层中发挥关键作用。 感官信息。该提案将检验听觉二阶丘脑形状的假设 整个听觉皮层的预测处理。首先，我将使用专门的转基因小鼠品系 标记二阶丘脑神经元以绘制二阶听觉的精确功能连接 丘脑（目标 1，K99）。接下来，我将开发一个声学增强现实家庭笼环境，让老鼠 可以快速学习多种预测行为。我将在自由移动鼠标的同时进行无线录音 多个发声运动以确定编码的感觉、运动和预测信息 在二阶听觉丘脑（目标 2，K99）。最后，我将进行同时多区域录制 并对行为小鼠的丘脑和皮质进行有针对性的神经干预，以确定预测的效果 计算通过丘脑皮质回路进行（目标 3，R00）。在我导师的指导下 团队，我在纽约大学制定了一项培训计划，该计划将为我提供所需的技术技能 完成这些目标并就分布式电路如何集成传感和 预测处理过程中的非感觉信息。拟议的培训计划还将为我提供 实现我的长期职业目标的概念框架和专业技能：调查如何分布 电路机械地协同工作，以实现健康和疾病中上下文相关的听觉处理 一位独立科学家。

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.