Mental, measurement, and model complexity in neuroscience

神经科学中的心理、测量和模型复杂性

• 批准号: 10002220
• 负责人: Vijay Balasubramanian
• 金额: $ 36.3万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002220
• 关键词: Address; Algorithms; Automobile Driving; Bayesian Modeling; Behavior; Behavioral; Benchmarking; Brain; Characteristics; Communities; Complex; Computer software; Data; Data Analyses; Data Collection; Data Set; Decision Making; Dimensions; Foundations; Goals; Guidelines; Human; Individual; Information Theory; Length; Machine Learning; Mathematics; Measurement; Measures; Methods; Modeling; Neurosciences; Neurosciences Research; Noise; Pattern; Physics; Process; Psyche structure; Reproducibility; Research Personnel; Rodent; Sampling; Series; Structure; System; Techniques; Time; Work; base; complex data; computer science; computerized tools; data modeling; data streams; data tools; design; information processing; insight; nonhuman primate; relating to nervous system; statistics; theories; tool

PROJECT SUMMARY Neuroscience is producing increasingly complex data sets, including measures and manipulations of sub- cellular, cellular, and multi-cellular mechanisms operating over multiple timescales and in the context of different behaviors and task conditions. These data sets pose several fundamental challenges. First, for a given data set, what are the relevant spatial, temporal, and computational scales in which the underlying information-processing dynamics are best understood? Second, what are the best ways to design and select models to account for these dynamics, given the inevitably limited, noisy, and uneven spatial and temporal sampling used to collect the data? Third, what can increasingly complex data sets, collected under increasingly complex conditions, tells us about how the brain itself processes complex information? The goal of this project is to develop and disseminate new, theoretically grounded methods to help researchers to overcome these challenges. Our primary hypothesis is that resolving, modeling, and interpreting relevant information- processing dynamics from complex data sets depends critically on approaches that are built upon understanding the notion of complexity itself. A key insight driving this proposal is that definitions of complexity that come from different fields, and often with different interpretations, in fact have a common mathematical foundation. This common foundation implies that different approaches, from direct analyses of empirical data to model fitting, can extract statistical features related to computational complexity that can be compared directly to each other and interpreted in the context of ideal-observer benchmarks. Starting with this idea, we will pursue three specific aims: 1) establish a common theoretical foundation for analyzing both data and model complexity; 2) develop practical, complexity-based tools for data analysis and model selection; and 3) establish the usefulness of complexity-based metrics for understanding how the brain processes complex information. Together, these Aims provide new theoretical and practical tools for understanding how the brain integrates information across large temporal and spatial scales, using formal, universal definitions of complexity to facilitate the analysis and interpretation of complex neural and behavioral data sets.

项目总结 神经科学正在产生越来越复杂的数据集，包括对亚 蜂窝、蜂窝和多蜂窝机制在多个时间尺度上运行，并且在 不同的行为和任务条件。这些数据集构成了几个根本性的挑战。首先，对于一个 给定的数据集，相关的空间、时间和计算尺度是什么？ 信息处理动态是最好理解的吗？第二，设计和选择最好的方法是什么 考虑到不可避免的有限、嘈杂和时空不均匀的情况下，考虑到这些动态的模型 用抽样来收集数据吗？第三，在日益复杂的数据集下，收集到的数据会越来越复杂 复杂的条件，告诉我们大脑本身是如何处理复杂信息的？这个项目的目标是 是开发和传播新的、理论上有基础的方法来帮助研究人员克服这些 挑战。我们的主要假设是，解析、建模和解释相关信息-- 处理复杂数据集的动态特性在很大程度上取决于构建在 理解复杂性本身的概念。推动这一提议的一个关键见解是复杂性的定义 它们来自不同的领域，而且往往有不同的解释，实际上有一个共同的数学 基金会。这种共同的基础意味着不同的方法，从对经验数据的直接分析 对模型进行拟合，可以提取与计算复杂度相关的统计特征进行比较 直接相互联系，并在理想观察者基准的背景下进行解释。从这个想法开始，我们 将追求三个具体目标：1)为分析数据和模型建立共同的理论基础 复杂性；2)开发实用的、基于复杂性的数据分析和模型选择工具；以及3)建立 基于复杂性的衡量标准对于理解大脑如何处理复杂信息的有用性。 总而言之，这些目标为理解大脑如何整合提供了新的理论和实践工具 跨大的时间和空间尺度的信息，使用正式的、通用的复杂性定义 帮助分析和解释复杂的神经和行为数据集。