Prefrontal contributions to contextual representation

项目摘要/摘要 这个应用程序描述了一个3年的培训计划，将使我，一个认知神经科学家与事先 在脑电图（EEG）培训，进行研究的背景记忆表征使用 神经成像（fMRI）和计算建模。EEG对于检查神经元的时间特性是有用的。 活动，但不能将活动定位到大脑的特定区域。在这个建议中，我将接受培训， 高空间分辨率神经成像技术（fMRI），这将使我能够发展神经系统的理论， 受空间和时间约束的函数。我还将建立在我以前的应用统计学位 并接受计算神经科学的额外培训，这将使我能够开发计算神经科学。 宏观电路层面的理论。我将由功能磁共振成像专家莎伦·汤普森-希尔博士监督 外侧前额叶皮层功能的实验学家和理论家，他在研究 语义记忆的上下文依赖性我将由安娜夏皮罗博士共同监督，她是 大脑的统计学习和计算建模。我建议研究前额叶皮层（PFC） 表示顺序呈现的视觉和听觉输入之间的统计依赖性。我将研究 顺序表征的时间范围和抽象水平如何在腹侧PFC中变化。 这将连接来自几篇文献的发现，从决策到情绪处理， 语言理解，在一个统一的框架内。此外，我将探讨是否'深'或 "浅"递归神经网络更好地捕捉腹侧PFC的敏感性， 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， 数据的两个神经成像实验的层次顺序处理。我会让参与者学习 抽象视觉（Aim 1a和b）和听觉（Aim 1b）分层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上测试神经对统计学习的敏感性 分析，比较学习前后对序列的神经反应。在目标2中，我将进行 目标1中神经成像数据的计算建模，保留数据以确保鲁棒性， 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比较， （"浅"）和多层（"深"）循环神经网络在与人类相同的序列上训练， 目标1。通过对上下文本身的神经表征进行建模，目前的提议将有助于填补一个关键空白 在我们对大脑如何预测即将到来的感官输入的理解中， 我们周围的世界。它还将告知我们对涉及前额叶的几种精神疾病的理解 皮质功能障碍和语境处理障碍，如精神分裂症、焦虑症和抑郁症。

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.