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.

一个关键的认知功能是期望。期望被认为是通过代理人的经验产生的 和学习一个已建立的理论模型，预测编码，指出大脑是不断建立 环境模型（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官数据相互作用。当PD与感测到的数据匹配时， 是正确的当它们不匹配时，将生成预测误差（PE）信号。该PE信号然后用于 更新预测，使大脑的内部模型可以更好地预测未来的感觉数据。 预测编码模型的影响是深远的。如果这个模型是正确的， 将我们对神经代码的理解从代表“环境状态”（例如，的 经典的Hubel和Wielton感受野模型），其中大脑执行“主动感知”并建立 世界的内部模型，测试他们对传入的感官数据。此外，预测编码 这个模型对我们理解疾病状态有很多意义。例如，自闭症可以理解为 作为一个失败的正确预测社会行动，因此，每一个社会互动是“令人惊讶的”。 关于如何在大脑中实现预测代码，存在各种理论。他们建议 不同的皮质层，通信流（前馈/反馈）和振荡动力学参与了 信令PE和PD。然而，几乎没有神经生理学数据支持这些模型。在这里，我建议 一个实验，通过改变与物体相关的概率来操纵预测， 匹配样本任务（目标1）。这将使我能够诱导不同强度的期望。我的初选 导师，厄尔米勒，我将接受培训，以执行使多区域，多层次的记录在猴子。然后我将 使用这些数据来研究期望是如何建立的，以及当它们被违反时会发生什么。在目标2中， 我的第二导师，南希科佩尔，我将使用计算建模来理解如何改变 输入的概率映射到一个同步发射的共同活动的细胞组（一个组件）。我们假设 不同的组合代表不同的预测。我们还假设， 组合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-抑制回路，我们将研究是否重新激活 该组装有节奏地发生，由深层皮质层中的β（15-30 Hz）振荡起搏。伽马 皮层浅层的振荡（40-90 Hz）可以通过信号传导帮助关闭电流预测（PD）。 预测误差（PE）。在目标3中，我们将测试是否中断β振荡（认为是PD的信号）， 闭环光遗传学抑制足以破坏预测的行为和神经元特征。 这种实验和生物物理建模的结合将为我们的研究做出重大贡献。 理解大脑功能的一个重要理论模型，预测编码。