Mental, measurement, and model complexity in neuroscience

项目摘要 神经科学正在产生越来越复杂的数据集，包括测量和操纵亚神经元。 细胞，细胞，和多细胞机制在多个时间尺度上运行， 不同的行为和任务条件。这些数据集带来了一些根本性的挑战。首先，对于A 给定的数据集，相关的空间，时间和计算尺度是什么，其中底层 信息处理动力学是最好的理解？第二，设计和选择的最佳方式是什么 考虑到不可避免的有限、嘈杂和不均匀的时空， 用于收集数据的抽样？第三，什么可以越来越复杂的数据集，收集下越来越多 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 是开发和传播新的，有理论基础的方法，以帮助研究人员克服这些问题， 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关键取决于建立在 理解复杂性本身的概念。推动这一提议的一个关键观点是， 来自不同的领域，通常有不同的解释，事实上有一个共同的数学 基金会这一共同基础意味着，不同的方法，从直接分析的经验数据， 模型拟合，可以提取相关的统计特征，计算复杂度可以比较 直接相互关联，并在理想观察者基准的上下文中进行解释。从这个想法开始，我们 我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; 3）建立 基于复杂性的度量对于理解大脑如何处理复杂信息的有用性。 总之，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， 有助于分析和解释复杂的神经和行为数据集。

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.