Prefrontal contributions to contextual representation

项目摘要/摘要 这份申请描述了一项为期3年的培训计划，这将使我成为一名认知神经科学家，在 接受脑电(EEG)培训，研究情境记忆表征 神经成像(FMRI)和计算建模。脑电可用于检测神经的时序特性 活动，但不能将活动局限于大脑的特定区域。在这项提议中，我将接受关于 高空间分辨率神经成像技术(FMRI)，这将使我能够发展神经理论 同时受空间和时间限制的功能。我还将在我以前的应用统计学学位的基础上再接再厉 并接受计算神经科学方面的额外培训，这将使我能够开发计算 宏观电路层面的理论。我将由功能核磁共振专家莎伦·汤普森-席尔博士指导 外侧前额叶皮质功能的实验学家和理论家，具有丰富的研究经验 语义记忆的语境依赖性。我将由安娜·夏皮罗博士共同监督，她是一位 大脑的统计学习和计算建模。我打算研究前额叶皮质(PFC)是如何 表示顺序呈现的视觉和听觉输入之间的统计相关性。我会检查一下 顺序表征的时间范围和抽象水平如何在腹侧PFC上发生变化。 这将把几篇文献的发现联系起来，从决策到情绪处理，以及 语言理解，在一个统一的框架内。此外，我还会探讨“深度”或 浅层递归神经网络更好地捕捉腹侧PFC的敏感度曲线，告知 大脑是否进行“深度”学习的问题。在目标1中，我将进行行为试验并收集 关于分层顺序处理的两个神经成像实验的数据。我将让参与者学习 抽象视觉(目标1a&b)和听觉(目标1b)的分级组织序列的统计特性 图像。然后，我使用模式相似性在每个层次级别测试神经对统计学习的敏感性 分析，比较学习前后序列的神经反应。在《目标2》中，我将指挥 对AIM 1中的神经成像数据进行计算建模，使用保持的数据以确保稳健性和 再现性。我将神经成像数据与来自单层的内部模型表示进行了比较 (“浅”)和多层(“深”)递归神经网络在与人类相同的序列上训练 目标1.通过对语境本身的神经表示进行建模，当前的提议将有助于填补一个关键的空白 在我们对大脑如何预测即将到来的感觉输入的理解中，能够快速处理 我们周围的世界。它还将使我们了解几种涉及前额叶的精神障碍 皮质功能障碍和语境处理障碍，如精神分裂症、焦虑和抑郁。

Project Abstract/Summary This application describes a 3-year training plan that will enable me, a cognitive neuroscientist with prior training in electroencephalography (EEG), to conduct research on contextual memory representation using neuroimaging (fMRI) and computational modeling. EEG is useful for examining the timing properties of neural activity, but cannot localize activity to specific regions of the brain. In this proposal, I will receive training on a high-spatial resolution neuroimaging technique (fMRI), which will allow me to develop theories of neural function that are constrained by both space and time. I will also build on my prior degree in applied statistics and receive additional training in computational neuroscience, which will enable me to develop computational theories at the macro-circuit level. I will be supervised by Dr. Sharon Thompson-Schill, an expert fMRI experimentalist and theorist of lateral prefrontal cortex function, who has extensive experience researching the context-dependent nature of semantic memory. I will be co-supervised by Dr. Anna Schapiro, an expert on statistical learning and computational modeling of the brain. I propose to examine how prefrontal cortex (PFC) represents statistical dependencies among sequentially presented visual and auditory input. I will examine how the temporal extent and level of abstraction of sequential representations changes across ventral PFC. This will connect findings from several literatures, ranging from decision-making to emotion processing and language comprehension, within a single unifying framework. In addition, I will explore whether ‘deep’ or ‘shallow’ recurrent neural networks better capture the sensitivity profile of ventral PFC, informing the question of whether the brain conducts ‘deep’ learning. In Aim 1, I will conduct behavioral piloting and collect data for two neuroimaging experiments on hierarchical sequential processing. I will have participants learn the statistical properties of hierarchically organized sequences of abstract visual (Aim 1a&b) and auditory (Aim 1b) images. I then test for neural sensitivity to statistical learning at each hierarchical level using pattern similarity analysis, comparing the neural response to the sequences before and after learning. In Aim 2, I will conduct computational modeling of the neuroimaging data in Aim 1, with held out data to ensure robustness and reproducibility. I compare the neuroimaging data to internal model representations derived from single-layer (‘shallow’) and multi-layer (‘deep’) recurrent neural networks trained on the same sequences as the humans in Aim 1. By modeling the neural representation of context itself, the current proposal will help fill a critical gap in our understanding of how the brain predicts upcoming sensory input, enabling rapid processing of the world around us. It will also inform our understanding of several psychiatric disorders that involve prefrontal cortex disfunction and disturbances of contextual processing, such as schizophrenia, anxiety and depression.