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

项目概要 随着神经科学研究的发展，神经科学研究也出现了爆炸式的发展。 核心大脑子系统功能的计算显式模型。然而不幸的是，有 没有相应地开发共享、验证和比较此类模型所需的工具，或者 将它们集成到系统级功能模型中。如果存在以下情况，这种共享、评估和整合是必要的： 计算建模工作不仅有助于生成可靠且准确的说明 大脑子系统的运作，还包括它们如何相互作用以产生更高的认知功能，以及如何 这种相互作用的破坏可能会引起精神功能的紊乱，这在精神病学和 神经系统疾病。该提案旨在通过开发 PsyNeuLink 来满足这一需求：一个开源、 基于Python的软件环境，可以轻松创建新模型、导入和/或重新实现 现有的，将它们集成到单个软件环境中，以促进头对头比较 比较模型，将互补模型组装成系统级模型，并作为 用于研究和教学的此类模型的记录和传播的公共存储库 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， PsyNeuLink 可以通过以下方式实现： i) 增强其应用程序编程接口 (API)，用于添加新的 统计分析工具和其他建模环境（例如 PyTorch、 紧急和 ACT-R； ii) 通过添加有影响力的模型的 PsyNeuLink 实现来丰富其库 神经子系统； iii) 开发一个公开可用的模拟练习工作簿，作为 PsyNeuLink 简介以及用于认知神经科学和计算精神病学课程的内容。 2） 通过开发保留其简单性和灵活性的自定义编译器来加速 PsyNeuLink，同时 显着提高其速度，使其适合大型复杂系统级模型的仿真， 以及参数估计、模型拟合和模型比较。 该项目将利用 Python 的强大功能和加速使用以及现代即时编译 开发专为满足系统级认知神经科学需求而设计的工具的方法 计算精神病学。这有望为系统级研究开辟新的机会—— 分析水平对于理解人类心理功能如何从相互作用中产生至关重要 神经子系统之间的关系，以及各个神经子系统的干扰如何影响这种相互作用， 其破坏几乎肯定是神经和精神疾病的关键因素。

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.