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cal模型不匹配，生成了预测误差（PE）信号。 对The Model Arel的启示，其中一些代表“环境状态”的代码（经典的Hubel和Wiesel接受场模型，一个Ine Ine Ine Int构建了世界的内部模型，对它们进行了测试，并根据感官数据进行测试。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”。 各种理论存在于大脑中的预测代码（进料/反馈），而振荡动力学在信号和PDS中。样本任务（AIM 1） ，使用我的次要导师Nancy Kopell，我使用计算模型来了解输入的概率如何映射到同步射击的共同活性群体（一个组件）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）。 Hz）通过信号预测误差（PE）在深层皮层中。 ）提供了闭环发育，以破坏预测的行为和神经元特征。