Computational Core

计算核心

• 批准号: 10377370
• 负责人: A DAVID REDISH
• 金额: $ 30.21万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10377370
• 关键词: Adaptive Behaviors; Address; Algorithms; Animals; Behavior; Behavioral; Behavioral trial; Cells; Clinical; Clinical Data; Computer Models; Data; Data Analyses; Data Set; Ensure; Equilibrium; Family; Functional disorder; Generations; Goals; Human; Infrastructure; Ion Channel; Lead; Learning; Measures; Membrane; Methodology; Minnesota; Modeling; Monkeys; Neuronal Dysfunction; Neuronal Plasticity; Neurons; Noise; Pharmaceutical Preparations; Population; Process; Property; Psychoses; Psychotic Disorders; Services; Statistical Data Interpretation; Synapses; System; Techniques; Translating; Universities; Work; base; behavioral outcome; data acquisition; data management; data modeling; experimental study; mathematical algorithm; neurophysiology; task analysis; theories; voltage

PROJECT SUMMARY: COMPUTATIONAL CORE The purpose of the COMPUTATIONAL CORE is to provide services for infrastructure and conceptual support to integrate the four Projects that form our Center. To do this, the Core will ensure that the trial-by-trial behavioral data collected from the four Projects (from the computationally informative DPX and Bandit tasks) are analyzed in a uniform and compatible manner, so that findings across Projects can be compared and modeled. It will also supply overall statistical support to ensure that statistical analyses are done appropriately. The Core consists of a Service Aim and a Modeling Aim. In the Service Aim, we propose to apply causal discovery analyses, a recently developed toolkit of mathematical algorithms that can infer explanatory relationships between co-occurring data parameters. Causal discovery analyses serve as a powerful inferential toolbox that can be applied to all of the PROJECTS independently, facilitating the generation of new hypotheses within and across PROJECTS. In the Modeling Aim, the Core will provide conceptual and modeling support for the theoretical underpinning of state representation dysfunctions relevant to psychosis. As noted in the OVERALL RESEARCH STRATEGY, we take a central computational perspective to translate across species and methodologies, using theoretical constructs to bridge the gap between neural dysfunction and observable manifestations of that dysfunction. To this end, the Core will integrate two existing models: an Algorithmic-Level Model that translates attractor dynamics to behavior, and a Neurophysiology-Level Model that translates neuronal properties to attractor dynamics. Our goal is to examine how neuronal-scale effects, such as those seen in the non-human animal projects (PROJECTS 1 & 2) translate into observable behavioral and neurophysiological effects in healthy and clinical populations, such as those seen in the human projects (PROJECTS 3 & 4), by merging these two models into an integrated model crossing levels, that can provide mechanistic explanatory power for how neurophysiological effects produce attractor dynamics that lead to behavioral outcomes.

项目摘要：计算核心 计算核心的目的是为基础设施和概念支持提供服务 整合组成我们中心的四个项目。为了做到这一点，核心将确保逐一审判 从四个项目收集的行为数据(从提供计算信息的DPX和Bandit任务中收集) 以统一和兼容的方式进行分析，以便跨项目的结果可以进行比较 模特儿。它还将提供全面的统计支助，以确保适当地进行统计分析。 核心由服务目标和建模目标组成。在服务目标中，我们建议应用因果关系 发现分析，这是最近开发的一个数学算法工具包，可以推断解释 共现数据参数之间的关系。因果发现分析是一种强大的 可独立应用于所有项目的推理工具箱，便于生成新的 项目内部和项目之间的假设。在建模目标中，核心将提供概念性和 与精神病相关的国家表征功能障碍的理论基础的模型支持。 正如在总体研究战略中所指出的，我们采取了中心计算的观点来翻译 跨物种和方法，使用理论构造来弥合神经功能障碍之间的差距 以及这种功能障碍的明显表现。为此，核心将整合现有的两个模型： 将吸引子动力学转化为行为的算法级模型和神经生理学级模型 这将神经元的特性转化为吸引子的动力学。我们的目标是研究神经元尺度的影响， 例如在非人类动物项目(项目1和2)中看到的那些转化为可观察到的行为 以及在健康和临床人群中的神经生理效应，例如在人类项目中看到的那些 (项目3和4)，通过将这两个模型合并为一个跨级别的综合模型，可以提供 对神经生理效应如何产生吸引子动力学的机械解释力 行为结果。