Mapping Algorithmic State Space in the Human Brain

映射人脑中的算法状态空间

• 批准号: 10406990
• 负责人: Sameer Anil Sheth
• 金额: $ 143.45万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406990
• 关键词: Acute; Address; Algorithms; Amaze; Anterior; Artificial Intelligence; Behavior; Behavioral; Brain; Cells; Chronic; Cinnamon - dietary; Cognitive; Complex; Computer Models; Data; Development; Devices; Elements; Engineering; Environment; Epilepsy; Etiology; Frequencies; Functional Magnetic Resonance Imaging; Goals; Hippocampus (Brain); Human; Implant; Individual; Inpatients; Instruction; Intelligence; Link; Location; Medial; Modeling; Monitor; Neurons; Neurosciences; Neurosciences Research; Participant; Patients; Pattern; Population; Prefrontal Cortex; Property; Psyche structure; Reporting; Research; Rodent; Role; Series; Signal Transduction; Structure; Task Performances; Telemetry; Temporal Lobe; Testing; Time; Variant; Work; base; cell cortex; cognitive neuroscience; cohort; computational basis; design; entorhinal cortex; experience; flexibility; innovation; mental representation; neuronal circuitry; neurophysiology; neurosurgery; novel; operation; physical state; predictive modeling; programs; rehearsal; relating to nervous system; response; virtual reality; virtual reality environment; way finding

Abstract Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is our ability to respond correctly to novel contextual situations on the first attempt, without prior rehearsal. 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. The overall goal of our research effort is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. Our previous U01 project (NS 108923) focused on how these programs of action are initially created. Our results thus far have revealed tantalizing notions of how the brain represents these programs and navigates through them. In this proposal, therefore, we focus on the question of how these mental programs are executed. Based on our preliminary findings and critical conceptual work, we propose that the medial temporal lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental programs, including concepts such as ‘rules’ and ‘locations’, to allow for effective navigation through the algorithm. These data suggest the existence of an ‘algorithmic state space’ represented in medial temporal and prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic state space in humans. By studying humans, we will profit from our species’ powerful capacity for generalization to understand how such state spaces are constructed. We therefore leverage the unique opportunities available in human neuroscience research to record from single cells and population-level signals, as well as to use intracranial stimulation for causal testing, to address this challenging problem. In Aim 1 we study the basic representations of algorithmic state space using a novel behavioral task that requires the immediate formation of unique plans of action. Aim 2 directly compares representations of algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic tasks in a virtual reality (VR) environment. Finally, in Aim 3, we test hypotheses regarding interactions between vPFC and MTL using intracranial stimulation.

抽象的 人类具有与环境灵活互动的非凡能力，这是一个令人信服的证明。 认知灵活性是指我们在第一次尝试时就能够正确应对新情境的能力，而无需 我们将这种能力称为“临时自编程”：“临时”是因为这些新的行为。 曲目会根据即时需求即时拼凑在一起，然后在不再使用时丢弃 必要的；“自编程”，因为大脑必须根据任务需求进行适当的配置 以及先前经验和/或指导的某种组合 我们研究工作的总体目标是 了解临时自编程行为的神经生理学和计算基础。 我们之前的 U01 项目 (NS 108923) 重点关注这些行动计划最初是如何制定的。 迄今为止的结果揭示了大脑如何代表这些程序并进行导航的诱人概念 因此，在本提案中，我们重点关注这些心理程序是如何存在的问题。 根据我们的初步发现和关键概念工作，我们建议内侧颞叶。 脑叶（MTL）和腹侧前额叶皮层（vPFC）创建了这些心理关键要素的表征。 程序，包括“规则”和“位置”等概念，以允许有效导航 算法。 这些数据表明存在以内侧时间和内侧时间表示的“算法状态空间” 该提案旨在了解该算法的神经生理学基础。 通过研究人类，我们将从人类强大的泛化能力中获益。 因此，我们利用现有的独特机会来了解如何构建此类状态空间。 在人类神经科学研究中记录单细胞和群体水平的信号，以及使用 颅内刺激进行因果测试，以解决这一具有挑战性的问题。 在目标 1 中，我们使用一种新颖的行为任务来研究算法状态空间的基本表示： 目标 2 要求立即形成独特的行动计划，直接比较不同的表述。 通过并置空间和算法的平衡版本，将算法状态空间与物理空间相结合 最后，在目标 3 中，我们测试有关之间交互的假设。 vPFC 和 MTL 使用颅内刺激。