PsyNeuLink: A Block Modeling Environment for Cognitive Neuroscience and Computational Psychiatry

项目摘要 与神经科学研究的增长同步的是， 核心脑子系统功能的计算显式模型。然而，不幸的是，有 不是共享、验证和比较此类模型所需的工具的相应开发，或者 将它们集成到系统级功能模型中。在以下情况下，这种共享、评估和集成是必要的 计算建模的努力将不仅在生成可靠和准确的关于如何 大脑各子系统的运作，以及它们如何相互作用以产生更高的认知功能，以及如何 这种相互作用的中断可能会引起精神疾病患者的精神功能障碍 神经紊乱。这个提议试图通过开发一种开源的、 基于Python的软件环境，可轻松创建新模型、导入和/或重新实现 现有的，将它们集成到一个软件环境中，以便于进行面对面的比较 将互补模型组装成系统级模型，并充当 用于记录和传播此类模型的公共存储库，用于研究和教学 目的(即出版、教育等)。 这些目标将在两个具体目标下实现：1)扩大建模工作的范围， 通过：i)增强其应用程序编程接口(API)以添加新的 到统计分析工具和其他建模环境的组件和接口(例如， Emerent和ACT-R；ii)通过添加有影响力的模型的心理神经链接实现来丰富其库 神经子系统；以及iii)开发一份公开可用的模拟练习工作簿，作为 《心理神经链接导论》，用于认知神经科学和计算精神病学课程。2) 通过开发一个定制的编译器来保持其简单性和灵活性，从而加速QiNeuLink 大大提高了速度，使其适用于大型复杂系统级模型的仿真， 并用于参数估计、模型拟合和模型比较。 这个项目将利用Python的强大功能和加速使用，以及现代的即时编译 方法开发一种专门针对系统级认知神经科学和 计算精神病学。这有望为系统一级的研究开辟新的机会。 分析水平对于理解人类心理功能如何从相互作用中浮现是至关重要的 神经系统之间的相互作用，以及单个神经系统的干扰如何影响这种相互作用， 它的中断几乎可以肯定是神经和精神障碍的关键因素。

Project Summary Paralleling the growth of neuroscience research, there has been an explosion in the development of computationally explicit models of the functions of core brain subsystems. Unfortunately, however, there has not been a commensurate development of the tools needed to share, validate, and compare such models, or integrate them into models of system-level function. Such sharing, evaluation, and integration are necessary if computational modeling efforts are to be useful not only in generating reliable and accurate accounts of how brain subsystems operate, but also of how they interact to give rise to higher cognitive functions, and how disruptions of such interactions may give rise to disturbances of mental function observed in psychiatric and neurological disorders. This proposal seeks to meet this need by developing PsyNeuLink: an open source, Python-based software environment that makes it easy to create new models, import and/or re-implement existing ones, integrate these within a single software environment that will facilitate head-to-head comparison of comparable models, the assembly of complementary models into system-level models, and serve as a common repository for the documentation and dissemination of such models for both research and didactic purposes (i.e., publication, education, etc.). These goals will be pursued under two Specific Aims: 1) Extend the scope of modeling efforts that PsyNeuLink can accommodate by: i) enhancing its application programmer interface (API) used to add new components and interfaces to statistical analysis tools and other modeling environments (such as PyTorch, Emergent and ACT-R; ii) enriching its Library by adding PsyNeuLink implementations of influential models of neural subsystems; and iii) developing a publicly available workbook of simulation exercises as both an introduction to PsyNeuLink and for use in Cognitive Neuroscience and Computational Psychiatry curricula. 2) Accelerate PsyNeuLink by developing a custom compiler that preserves its simplicity and flexibility, while dramatically increasing its speed, to make it suitable for simulation of large and complex system-level models, and for parameter estimation, model fitting, and model comparison. This project will exploit the power and accelerating use of Python, and modern just-in-time compilation methods to develop a tool designed specifically for the needs of systems-level Cognitive Neuroscience and Computational Psychiatry. This promises to open up new opportunities for research at the systems-level — a level of analysis that is crucial both for understanding how human mental function emerges from the interplay among neural subsystems, and how disturbances of individual neural subsystems impact this interplay, disruptions of which are almost certainly a critical factor in neurologic and psychiatric disorders.