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

预测编码和感官意识中的受体、微电路和分层连接

基本信息

批准号：
10459282
负责人：
Yuri B Saalmann
金额：
$ 39.7万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-15 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10459282
关键词：
Adrenergic Receptor Agonist Anesthesia procedures Anesthetics Area Auditory Auditory area Awareness Behavior Brain Brain Diseases Code Complex Computer Models Conscious Cues Data Delirium Dementia Dexmedetomidine Disinhibition Dose Dreams Electroencephalography Environment Experimental Designs Feedback Future Goals Health Human Impairment Individual Interneurons Intervention Ketamine Link Macaca Machine Learning Measures Mediating Mental Depression Modality Modeling Molecular Target Monitor N-Methyl-D-Aspartate Receptors Neurons Organ Parietal Lobe Pathway interactions Perception Pharmacology Process Propofol Public Health Pulvinar structure Reaction Time Reporting Research Role Schizophrenia Sedation procedure Sensory Sensory Process Shapes Signal Transduction Sleep Study models System Temporal Lobe Testing Thalamic Nuclei Unconscious State Update antagonist base effective therapy experience experimental study frontal lobe human data improved innovation innovative technologies insight multi-electrode arrays neural correlate neuromechanism neurophysiology nonhuman primate paired stimuli postsynaptic prevent receptor relating to nervous system response sedative sensory cortex sensory stimulus therapeutic development transmission process treatment strategy visual stimulus

项目摘要

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.

概括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来自我们的感觉器官接收到的信息。按照这种观点，低级大脑区域（例如初级感觉 cortex）代表对象的基本特征，这些特征在连续的处理阶段中得到详细说明，直到在高层区域（例如额叶皮层），表征变得越来越复杂。另一种观点是预测编码（PC），我们对环境进行建模以生成感官预测。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级区域。一个预测是与感官信息不匹配会导致预测错误。该错误信号从低发送到高级大脑区域来更新我们的环境模型，从而改进未来的预测，以最大限度地减少错误。建模研究表明 PC 是一种快速有效的处理感官信息的方式，并且 PC 提供理解睡眠和麻醉的创新假设，特别是在意识分离的情况下发生（没有意识到环境的意识），就像做梦一样。 PC 也有很大的前景概念化和治疗大脑疾病，包括精神分裂症和抑郁症。但关键的核心特征 PC 的作用尚未经过实证检验，并且对潜在的神经机制知之甚少。目标该项目的主要目的是表征 PC 的神经动力学、电路和受体。有两个原则假设。首先，预测取决于 N-甲基-D-天冬氨酸受体 (NMDAR)，因为 NMDAR 影响生成预测的高级大脑区域的活动，并且 NMDAR 丰富了接受预测的较低级别区域的神经元。其次，在断开的意识中，从低级大脑区域到高级大脑区域的信息传输中断，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。这些故障阻碍了预测和感官信息的比较，以及将预测错误传递到高级大脑区域。为了检验这些假设，我们使用跨物种将细胞、电路和系统级别与行为联系起来的实验设计。我们将表演脑电图、机器学习和计算建模来定义 PC 的神经基础人类执行预测任务。然后我们将使用不同的麻醉剂来操纵PC 机制，建立受体、大规模大脑网络和 PC 之间的因果关系。在与此同时，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 任务和药理学干预措施来测量细胞和回路水平对个人电脑的贡献。研究 PC 将阐明感知的基本机制，提供指导多种健康状况的治疗开发的重要见解。