Mapping Algorithmic State Space in the Human Brain

映射人脑中的算法状态空间

• 批准号: 10613535
• 负责人: Sameer Anil Sheth
• 金额: $ 143.49万
• 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/10613535
• 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; Human; Implant; Individual; Inpatients; Instruction; Intelligence; Link; Location; Maps; 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; cell cortex; cognitive neuroscience; cohort; computational basis; design; entorhinal cortex; experience; flexibility; innovation; mental representation; neural; neuronal circuitry; neurophysiology; neurosurgery; novel; operation; physical state; predictive modeling; programs; rehearsal; 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.

摘要 人类具有与环境灵活互动的非凡能力。一个令人信服的证明 认知灵活性是我们在第一次尝试时对新的情境做出正确反应的能力， 提前排练我们把这种能力称为“特别自我编程”：“特别”是因为这些新的行为 剧目是匆忙拼凑起来的，基于即时的需求，然后在不再需要时被丢弃。 “自我编程”，因为大脑必须根据任务需求适当地配置自己 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 了解神经生理学和计算基础的特设自我编程的行为。 我们之前的U 01项目（NS 108923）专注于这些行动计划最初是如何创建的。我们 到目前为止，研究结果已经揭示了大脑如何表达这些程序和导航的诱人概念。 通过他们。因此，在这个建议中，我们关注的问题是，这些心理程序是如何运作的？ 处决了被基于我们的初步研究结果和关键的概念工作，我们提出，内侧颞叶 额叶（MTL）和腹侧前额叶皮层（vPFC）创造了这些心理活动的关键要素的表征。 程序，包括“规则”和“位置”等概念，以允许在 算法 这些数据表明，存在一个“算法状态空间”表示在内侧颞叶和 前额区该提案旨在了解这种算法的神经生理学基础 人类的状态空间通过研究人类，我们将受益于我们物种强大的泛化能力 来理解这种状态空间是如何构造的。因此，我们充分利用现有的独特机会 在人类神经科学研究中，记录来自单个细胞和群体水平的信号，以及使用 颅内刺激的因果关系测试，以解决这一具有挑战性的问题。 在目标1中，我们使用一种新的行为任务来研究算法状态空间的基本表示， 需要立即制定独特的行动计划。目标2直接比较 通过并列空间和算法的平衡版本， 虚拟现实（VR）环境中的任务。最后，在目标3中，我们测试了关于以下因素之间相互作用的假设： 使用颅内刺激的vPFC和MTL。

项目成果

Benefits of sharing neurophysiology data from the BRAIN Initiative Research Opportunities in Humans Consortium.

共享来自人类联盟 BRAIN Initiative 研究机会的神经生理学数据的好处。

• DOI: 10.1016/j.neuron.2023.09.029
• 发表时间: 2023
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Rahimzadeh,Vasiliki;Jones,KathrynMaxson;Majumder,MaryA;Kahana,MichaelJ;Rutishauser,Ueli;Williams,ZivM;Cash,SydneyS;Paulk,AngeliqueC;Zheng,Jie;Beauchamp,MichaelS;Collinger,JenniferL;Pouratian,Nader;McGuire,AmyL;Sheth,Sam
• 通讯作者: Sheth,Sam