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: 10224537
• 负责人: Andre Moraes Bastos
• 金额: $ 6.85万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-07 至 2021-04-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10224537
• 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; 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; biophysical model; 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

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. Here, I propose an experiment to manipulate predictions by changing the probabilities associated with objects in a delayed- match-to-sample task (Aim 1). This will allow me to induce expectations of varying strengths. With my primary mentor, Earl Miller, I will be trained to perform make multi-area, multi-laminar recordings in monkeys. I will then use 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 will use computational modeling to understand how the changing probability of inputs map on to a synchronously firing co-active group of cells (an assembly). We hypothesize that different assemblies represent different predictions. We also hypothesize 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 will investigate 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, we 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. This combination of experiments and biophysical modeling is poised to significantly contribute to our understanding of an important theoretical model of brain function, predictive coding.

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产生的 和学习。建立的理论模型，预测性编码，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。然后将此PE信号用于 更新预测，以便大脑的内部模型可以更最佳地预测未来的感官数据。 对预测编码模型的影响是深远的。如果模型是正确的，那将从根本上 将我们对神经代码的理解转移到代表“环境状态”的神经守则（例如， 经典的Hubel和Wiesel接收场模型），大脑执行“主动灵敏度”并构建一个模型 世界内部模型，测试它们与传入的感觉数据。另外，预测性编码 模型对我们对疾病状态的理解有许多影响。例如，可以理解自闭症 作为正确预测社会行为的失败，并且结果，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。他们提出了这一点 独特的皮质层，交流流（前馈/反馈）和振荡动态参与 信号和PD。但是，几乎没有神经生理数据来支持这些模型。我建议在这里 通过更改与对象相关的可能性来操纵预测的实验 匹配到样本任务（AIM 1）。这将使我影响对各种优势的期望。与我的小学一起 导师，伯爵·米勒（Earl Miller），我将接受培训，可以在猴子中进行多个区域的多层次录音。然后我会 使用这些数据研究期望的建立方式以及违反期望的情况。在AIM 2中， 我的次要导师南希·科佩尔（Nancy Kopell），我将使用计算建模来了解如何改变 输入的概率映射到同步触发的共同活性单元组（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 组装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，我们将研究是否重新激活 组装有节奏地发生，以深层皮质层的β（15-30 Hz）振荡节奏。伽玛 浅表皮质层中的振荡（40-90 Hz）可以通过信号传导来关闭当前预测（PD） 预测错误（PE）。在AIM 3中，我们将测试是否会中断β振荡（以为pd发出信号） 闭环光遗传学抑制足以破坏预测的行为和神经元特征。 实验和生物物理建模的这种组合被中毒，可以显着促进我们的 了解大脑功能，预测编码的重要理论模型。