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

项目摘要 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和学习而产生的。一个已建立的理论模型--预测编码--指出，大脑不断地构建环境的模型(表示不断变化的预期)。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与感测数据匹配时，预期是正确的。当它们不匹配时，生成预测误差(PE)信号。然后，这个PE信号被用来更新预测，这样大脑的内部模型就可以更优化地预测未来的感觉数据。 对预测编码模型的影响是深远的。如果该模型是正确的，它将从根本上改变我们对神经代码的理解，从代表“环境状态”(例如，经典的Hubel和威塞尔接受场模型)转变为大脑执行“主动感知”并建立世界的内部模型，并根据传入的感觉数据对其进行测试。此外，预测编码模型对于我们对疾病状态的理解有很多启示。例如，自闭症可以被理解为未能正确预测社会行为，因此，每一次社会互动都是令人惊讶的。 关于如何在大脑中实施预测代码，存在各种理论。他们认为，不同的大脑皮层、通信流(前馈/反馈)和振荡动力学参与了PE和PD的信号传递。然而，很少有神经生理学数据支持这些模型。在这项授权的K99部分中，我通过更改与延迟匹配到样本任务(目标1)中的对象相关的概率来操纵预测。这让我对不同的强项产生了期望。在我的主要导师厄尔·米勒的指导下，我接受了在猴子身上进行多区域、多层面录音的培训。然后，我使用这些数据来研究期望是如何建立的，以及当期望被违背时会发生什么。在目标2中，我和我的二级导师南希·科佩尔一起，使用计算建模来理解输入的变化概率如何映射到同步启动的协同活动的细胞组(集合)。我们假设不同的程序集代表不同的预测。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，由于组件中细胞之间的兴奋-抑制环路，我们调查了组件的重新激活是否有节奏地发生，由皮层深层的β(15-30赫兹)振荡控制。皮层浅层的伽马振荡(40-90赫兹)可以通过发出预测误差(PE)信号来帮助关闭电流预测(PD)。在目标3中，一个独立的目标将是我在R00拨款部分的重点，我将测试用闭合环光遗传抑制来中断β振荡(被认为是信号PD)是否足以扰乱预测的行为和神经元特征。这些实验将极大地促进我们对预测编码的理解。

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.