Testing the Mechanisms, Layers, and Frequencies of Prediction Encoding and its Violation

测试预测编码的机制、层和频率及其违规

• 批准号: 10439967
• 负责人: Andre Moraes Bastos
• 金额: $ 24.9万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10439967
• 关键词: Architecture; Area; Behavior; Behavioral; Beta Rhythm; Biophysical Process; Biophysics; Brain; Brain Diseases; Cells; Code; Cognition Disorders; Communication; Computer Models; Coupled; Cues; Data; Disease; Electrophysiology (science); Environment; Failure; Feedback; Frequencies; Future; Goals; Grant; Impairment; Implanted Electrodes; Interruption; Knowledge; Lead; Learning; Maps; Mentors; Modeling; Monkeys; Nervous System Physiology; Neurons; Parietal Lobe; Periodicity; Prefrontal Cortex; Probability; Role; Sampling; Sensory; Signal Transduction; Social Interaction; Stimulus; Testing; Theoretical model; Training; Update; Visual Cortex; area V4; autism spectrum disorder; base; cell assembly; cognitive function; expectation; experience; experimental study; flexibility; insight; model building; network models; neural circuit; neural network; neuromechanism; neurophysiology; novel therapeutics; optogenetics; receptive field; relating to nervous system; social; theories

Project Summary A key cognitive function is expectation. Expectation is thought to be generated through an agent’s experiences and learning. An established theoretical model, predictive coding, states that the brain is constantly building models (signifying changing expectations) of the environment. The brain does this by forming predictions (PD). These predictions interact with incoming sensory data. When the PD matches the sensed data, the expectation is correct. When they do not match, a prediction error (PE) signal is generated. This PE signal is then used to update the prediction, so that the brain’s internal model can more optimally predict future sensory data. The implications for the predictive coding model are far-reaching. If the model is correct, it would fundamentally shift our understanding of the neural code from one that represents the “state of the environment” (e.g., the classic Hubel and Wiesel receptive field model) to one in which the brain performs “active sensing” and builds internal models of the world, testing them against incoming sensory data. In addition, the predictive coding model has many implications for our understanding of disease states. For example, autism can be understood as a failure in correctly predicting social actions, and as a result, every social interaction is “surprising”. Various theories exist about how a predictive code could be implemented in the brain. They propose that distinct cortical layers, flow of communication (feedforward/feedback), and oscillatory dynamics are involved in signaling PEs and PDs. However, little neurophysiological data exist to support these models. In the K99 portion of this grant, I manipulated predictions by changing the probabilities associated with objects in a delayed-match-to-sample task (Aim 1). This allowed me to induce expectations of varying strengths. With my primary mentor, Earl Miller, I was trained to perform make multi-area, multi-laminar recordings in monkeys. I then used these data to study how expectations are built and what happens when they are violated. In Aim 2, with my secondary mentor, Nancy Kopell, I used computational modeling to understand how the changing probability of inputs map on to a synchronously firing co-active group of cells (an assembly). We hypothesized that different assemblies represent different predictions. We also hypothesized that the strength of each assembly will represent the probability of a particular stimulus (thereby forming the neural basis of PD). Finally, due to the excitatory-inhibitory loops between cells in an assembly, we investigated whether re-activations of the assembly occur rhythmically, paced by a beta (15-30 Hz) oscillation in deep cortical layers. Gamma oscillations (40-90 Hz) in superficial cortical layers could help switch off the current prediction (PD) by signaling prediction error (PE). In Aim 3, an independent aim that will be my focus during the R00 portion of the grant, I will test whether interrupting beta oscillations (thought to signal PD) with closed-loop optogenetic inhibition is sufficient to disrupt the behavioral and neuronal signatures of prediction. These experiments are poised to significantly contribute to our understanding of predictive coding.

项目摘要 关键的认知功能是期望。人们认为，期望是通过代理商的经验和学习产生的。建立的理论模型预测编码指出，大脑在不断建立环境的模型（表示不断变化的期望）。大脑通过形成预测（PD）来做到这一点。这些预测与传入的感觉数据相互作用。当PD匹配感应数据时，期望是正确的。当它们不匹配时，会生成一个预测错误（PE）信号。然后使用此PE信号来更新预测，以便大脑的内部模型可以更优化地预测未来的感官数据。 对预测编码模型的影响是深远的。如果该模型正确，它将从根本上将我们对神经代码的理解从代表“环境状态”（例如，经典的Hubel和Wiesel接收场模型）转变为大脑执行“主动灵敏度”并构建世界内部模型并对其进行感性数据测试它们的内部模型的一种。此外，预测编码模型对我们对疾病状态的理解有许多影响。例如，自闭症可以理解为正确预测社会行为的失败，因此，每种社会互动都是令人惊讶的。 关于如何在大脑中实现预测代码的各种理论。他们提出，信号PES和PDS涉及不同的皮质层，通信流动（前馈/反馈）和振荡动态。但是，几乎没有神经生理数据来支持这些模型。在这笔赠款的K99部分中，我通过更改与对象相关的可能性来操纵预测，以延迟匹配样本到样本任务（AIM 1）。这使我能够影响对各种优势的期望。凭借我的主要心理，伯爵·米勒（Earl Miller），我接受了培训，可以在猴子中进行多个区域的多层次录音。然后，我使用这些数据来研究建立期望的方式以及违反期望的情况。在AIM 2中，借助我的继发性精神，南希·科佩尔（Nancy Kopell），我使用计算建模来了解输入的变化概率如何映射到同步射击的共同活性单元组（一个组装）。我们假设不同的组件代表不同的预测。我们还假设每个组件的强度将代表特定刺激的概率（从而形成PD的神经基础）。最后，由于组装中细胞之间的兴奋性抑制环路，我们在深层皮质层中的β（15-30 Hz）振荡的节奏进行了节奏的重新激活。浅表皮质层中的γ振荡（40-90 Hz）可以通过信号预测误差（PE）来帮助关闭当前预测（PD）。在AIM 3中，一个独立的目标将是我在赠款的R00部分中的重点，我将测试用闭环光遗传学抑制的中断β振荡（以为pd表示信号）是否足以破坏预测的行为和神经元信号。这些实验被中毒以显着有助于我们对预测编码的理解。