Mechanisms of Rapid, Flexible Cognitive Control in Human Prefrontal Cortex

人类前额叶皮层快速、灵活的认知控制机制

• 批准号: 9792299
• 负责人: Sameer Anil Sheth
• 金额: $ 66.56万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9792299
• 关键词: Address; Alzheimer' s disease model; Amaze; Animals; Anxiety Disorders; Architecture; Artificial Intelligence; Basic Science; Behavior; Behavioral; Brain; Cities; Code; Cognitive; Communities; Complex; Computer Architectures; Computer Simulation; Cues; Custom; Development; Elements; Engineering; Environment; Faculty; Feedback; Functional disorder; Goals; Human; Individual; Instruction; Intelligence; Investigation; Knowledge; Lead; Learning; Left; Light; Liquid substance; Literature; Logic; London; Machine Learning; Measurement; Memory; Methodology; Modeling; Mood Disorders; Neural Network Simulation; Neurons; Neurosciences; Performance; Phase; Physiological; Prefrontal Cortex; Process; Programmed Learning; Psychological reinforcement; Psychotic Disorders; Reaction Time; Research; Research Proposals; Resolution; Response to stimulus physiology; Side; Source; Structure; Surface; System; Testing; Time; Training; Translating; Visit; Work; addiction; base; behavioral study; cognitive control; cognitive process; computational basis; design; experience; flexibility; human subject; innovation; insight; neuronal circuitry; neurophysiology; neuropsychiatric disorder; neuropsychiatry; neurosurgery; novel; operation; programs; recurrent neural network; relating to nervous system; response; spatiotemporal; targeted treatment; theories; tool

Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is our ability to perform complex, yet previously un-practiced tasks successfully on the first attempt. We refer to this ability as `ad hoc self-programming': `ad hoc' because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; `self-programming' because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. This type of learning differs importantly from trial-and-error learning, in which responses are sculpted incrementally, based on feedback from previous attempts. In comparison to trial-and-error learning, much less is known about ad hoc self- programmed learning, but it clearly represents a fundamental feature of human intelligence. The overall goal of our research proposal is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. There have been significant barriers to the study of this topic. Among them are the difficulty of studying these processes in animals who require training (which by definition precludes single-trial self- programming), and the lack of access to opportunities with sufficient spatiotemporal resolution to study neuronal processes in humans. The proposed research seeks to address this gap. We leverage critical advances in neuroscience, neurosurgery, engineering, and computational modelling, including: 1) availability of a large-scale recording platform enabling simultaneous recordings of 100+ neurons from the cortical surface; 2) opportunities to record from dorsolateral prefrontal cortex (dlPFC) in human subjects engaged in a custom-designed behavioral task; 3) developments borrowed from the artificial intelligence community to create advanced neural network models of complex cognitive processes. By applying these innovative methodologies, we focus on addressing our overall goal with three Specific Aims. In Aim 1, we determine what information about the structure of a novel, complex, instructed task is represented in human dlPFC neuronal activity. We also determine how and when this information is encoded, in terms of spiking activity, oscillatory activity, or coherence between the two. In Aim 2, we determine the relationship between these neuronal representations and behavior. We investigate how the robustness and timing of the emergence of required neural representations relates to response accuracy and reaction time. In Aim 3, we develop a computational model of ad hoc self-programmed learning. To do so, we borrow from recent insights in the AI world regarding prefrontal network structure, and also apply our developing understanding of neural representations from the previous Aims. We expect that this innovative approach will revolutionize our understanding of this amazing capacity for immediate, configurable learning that characterizes our everyday lives. In doing so, we will develop new strategies to study mechanisms of rapid, flexible cognitive control in general. A better understanding of human cognitive control and its nuanced capacities will naturally translate into an appreciation of deficiencies in these processes, and how they manifest in the form of neuropsychiatric disorders. This appreciation can then lead to the development of rational, targeted therapies.

人类具有与环境灵活互动的非凡能力。令人信服的证明， 这种认知灵活性是指我们能够成功地完成复杂的，但以前从未实践过的任务的能力。 第一次尝试。我们把这种能力称为“特设自我编程”：“特设”是因为这些新的 基于即时需求，行为剧目被匆忙拼凑在一起，然后被丢弃 当不再需要时;“自我编程”，因为大脑必须根据 根据任务要求以及先前经验和/或指令的某种组合。这种类型的学习不同 重要的是从试错学习中，反应是基于反馈逐步塑造的， 从以前的尝试。与试错学习相比，对自组织学习的了解要少得多。 程序学习，但它显然代表了人类智能的一个基本特征。整体 我们研究计划的目标是了解ad hoc的神经生理学和计算基础。 自我编程的行为 对这一专题的研究一直存在重大障碍。其中就包括学习的困难 需要训练的动物中的这些过程（根据定义，这排除了单次自我试验， 编程），以及缺乏足够的时空分辨率来研究的机会 人类的神经过程。 拟议的研究旨在解决这一差距。我们利用神经科学的重要进展， 神经外科、工程和计算建模，包括：1）大规模记录的可用性 能够同时记录来自皮层表面的100多个神经元的平台; 2） 记录从背外侧前额叶皮层（dlPFC）在人类受试者从事定制设计的 行为任务; 3）从人工智能社区借来的发展，以创建先进的 复杂认知过程的神经网络模型。 通过应用这些创新方法，我们专注于实现我们的总体目标， 具体目标。在目标1中，我们确定了关于一个新颖的、复杂的、有指导意义的结构的什么信息， 任务在人类dlPFC神经元活动中表现。我们还确定如何以及何时将这些信息 编码，在尖峰活动，振荡活动，或两者之间的连贯性。在目标2中， 确定这些神经元表征和行为之间的关系。我们调查了 所需神经表征出现的鲁棒性和时间与响应准确性有关 和反应时间。在目标3中，我们开发了一个特设自编程学习的计算模型。做 因此，我们借鉴了人工智能领域最近关于前额叶网络结构的见解，并应用我们的 从以前的目标发展神经表征的理解。 我们期望这种创新的方法将彻底改变我们对这种惊人能力的理解 即时的、可配置的学习是我们日常生活的特征。在此过程中，我们将开发新的 策略，以研究一般的快速，灵活的认知控制机制。更好地了解 人类的认知控制及其微妙的能力将自然地转化为对 这些过程中的缺陷，以及它们如何以神经精神障碍的形式表现出来。这 然后，欣赏可以导致合理的，有针对性的治疗方法的发展。