Mechanisms of Rapid, Flexible Cognitive Control in Human Prefrontal Cortex

人类具有与环境灵活互动的非凡能力。令人信服的演示 这种认知灵活性是我们成功地完成复杂的、但以前从未练习过的任务的能力。 第一次尝试。我们将这种能力称为‘特别自编程’：‘特别’，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 当不再需要的时候；‘自我编程’，因为大脑必须根据 关于任务需求以及先前经验和/或指导的某种组合。这种学习类型是不同的 重要的是来自试错学习，在这种学习中，响应是基于反馈逐渐形成的 从之前的尝试中。与试错式学习相比，人们对临时自我学习的了解要少得多 程序化学习，但它显然代表了人类智力的一个基本特征。整体而言 我们的研究计划的目标是了解特别的神经生理学和计算基础 自我编程的行为。 对这一主题的研究一直存在重大障碍。其中就有学习上的困难 需要训练的动物的这些过程(根据定义，这排除了单次自我试验 方案编制)，以及缺乏获得具有足够时空分辨率的机会进行研究 人类的神经元突起。 这项拟议的研究旨在解决这一差距。我们利用神经科学的关键进展， 神经外科、工程学和计算建模，包括：1)大规模录音的可用性 支持从皮质表面同时记录100个神经元的平台；2)有机会 定制设计受试者背外侧前额叶皮质(DlPFC)记录 行为任务；3)借用人工智能社区的开发来创建高级 复杂认知过程的神经网络模型。 通过应用这些创新的方法，我们专注于通过三个方面来实现我们的总体目标 明确的目标。在目标1中，我们确定关于新奇的、复杂的、被指导的 任务存在于人的dlPFC神经元活动中。我们还确定此信息的方式和时间 编码，根据尖峰活动、振荡活动或两者之间的一致性。在目标2中，我们 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稳健性和时机与反应的准确性有关 和反应时间。在目标3中，我们开发了一个自组织自编程学习的计算模型。去做 因此，我们借鉴了人工智能世界关于前额叶网络结构的最新见解，并应用我们的 从先前的目标发展对神经表征的理解。 我们希望这种创新的方法将彻底改变我们对这一惊人能力的理解 即时、可配置的学习，这是我们日常生活的特点。在此过程中，我们将开发新的 研究快速、灵活的认知控制机制的一般策略。更好地理解 人类的认知控制及其微妙的能力自然会转化为对 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。

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.