Receptors, microcircuits and hierarchical connectivity in predictive coding and sensory awareness

摘要 我们如何理解周围世界的标准观点侧重于重建我们的环境 从我们的感觉器官接收到的信息。在这一观点中，低水平的大脑区域(例如，初级感觉 皮层)代表对象的基本特征，这些特征在连续的处理阶段中被详细阐述，直到 在高水平区域(如额叶皮质)，表征变得越来越复杂。另一种观点是 预测编码(PC)，在其中我们对我们的环境进行建模以生成感觉预测。在PC中，高级 大脑区域产生感觉活动的预测，并将其传输到低水平区域。一种预测 与感官信息不匹配会导致预测误差。该误差信号从低到低发送 高级大脑区域来更新我们的环境模型，从而提高对未来的预测，将其降至最低 错误。建模研究表明，PC是处理感觉信息的一种快速有效的方式，而PC提供了 理解睡眠和麻醉的创新假设，特别是在意识断开的情况下 发生(意识而不知道环境)，就像做梦一样。PC也有很大的前景 概念化和治疗脑部疾病，包括精神分裂症和抑郁症。但关键的核心特征 PC的神经机制尚未经过实证检验，对其潜在的神经机制知之甚少。目标是 这项提议的项目的目的是描述神经动力学、电路和感受器使PC成为可能。确实有 两个基本假设。首先，预测依赖于N-甲基-D-天冬氨酸受体(NMDAR)，因为 NMDAR影响产生预测的高级大脑区域的活动，而NMDAR 丰富了较低水平区域接收预测的神经元。第二，在断开的意识中，一个 从低水平到高水平大脑区域的信息传递细目，以及 每个区域内的计算解释了为什么我们的环境模型不会被外部感官更新 信息。这些分解阻止了预测和感官信息的比较，以及 将预测误差传递到高级大脑区域。为了检验这些假设，我们使用了一个跨物种的 将细胞、电路和系统层次与行为联系起来的实验设计。我们将表演 脑电、机器学习和计算建模确定PC的神经基础 执行预测任务的人类。然后我们将使用不同的麻醉剂和不同的麻醉剂操纵PC 机制，建立受体、大规模脑网络和PC之间的因果关系。在……里面 同时，我们将同时记录非人类灵长类动物的感觉和高级大脑区域的活动 (NHP)使用相同的PC任务和药物干预来测量细胞和电路水平 对个人电脑的贡献。研究PC将阐明感知的基本机制，提供 指导针对多种健康状况的治疗发展的重要见解。

SUMMARY The standard view of how we make sense of the world around us focuses on reconstructing our environment from the information received by our sensory organs. In this view, low-level brain areas (e.g., primary sensory cortex) represent basic features of objects, which are elaborated on in successive processing stages, until representations become increasingly complex in high-level areas (e.g., frontal cortex). An alternative view is predictive coding (PC), in which we model our environment to generate sensory predictions. In PC, high-level brain areas generate predictions of sensory activity and transmit them to low-level areas. A prediction that does not match the sensory information gives rise to a prediction error. This error signal is sent from low- to high-level brain areas to update the model of our environment, thereby improving future predictions to minimize errors. Modeling studies show PC is a fast and efficient way to process sensory information, and PC provides innovative hypotheses for understanding sleep and anesthesia, particularly when disconnected consciousness occurs (consciousness without awareness of the environment), like dreaming. PC also holds great promise for conceptualizing and treating brain disorders, including schizophrenia and depression. But key central features of PC have not been empirically tested and little is known about the underlying neural mechanisms. The goal of the proposed project is to characterize the neural dynamics, circuits and receptors enabling PC. There are two principle hypotheses. First, predictions depend on N-methyl-D-aspartate receptors (NMDAR) because NMDAR influence the activity of high-level brain areas where predictions are generated, and NMDAR are enriched on neurons in lower-level areas receiving predictions. Second, in disconnected consciousness, a breakdown of information transmission from low-level to high-level brain areas, as well as a breakdown of computations within each area, explains why models of our environment are not updated by external sensory information. These breakdowns prevent the comparison of predictions and sensory information, as well as the transmission of prediction errors to high-level brain areas. To test these hypotheses, we use a cross-species experimental design connecting cellular, circuit and systems levels to behavior. We will perform electroencephalography, machine learning and computational modeling to define the neural basis of PC in humans performing prediction tasks. Then we will manipulate PC using different anesthetic agents with diverse mechanisms, establishing causal relationships between receptors, large-scale brain networks and PC. In parallel, we will simultaneously record activity from sensory and high-level brain areas of non-human primates (NHPs) using the same PC tasks and pharmacological interventions to measure cellular and circuit level contributions to PC. Investigating PC will illuminate the fundamental mechanisms of perception, providing critical insights to guide therapeutic development for multiple health conditions.