Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany.
Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
PLoS Comput Biol. 2022 Sep 8;18(9):e1010086. doi: 10.1371/journal.pcbi.1010086. eCollection 2022 Sep.
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
可持续的神经元网络计算模型研究需要发表的模型具有可理解性、可重复性和可扩展性。关于数学概念和假设、算法实现或参数化的缺失细节或模糊性会阻碍进展。这种缺陷很常见，原因之一是缺乏适用于模型描述的现成标准和工具。我们的工作旨在推进网络连接的完整和简洁描述，同时指导仿真软件和神经形态硬件系统中连接例程的实现。我们首先回顾了 ModelDB 和 Open Source Brain 存储库中计算神经科学社区提供的模型，并调查了手稿和代码中相应的连接结构及其描述。该评论包括具有不同神经解剖细节水平的网络的连接，并揭示了连接在现有描述语言和模拟器接口中的抽象方式。我们发现，已发表的连接描述中有相当一部分是模糊的。基于此回顾，我们为确定性和概率连接网络以及嵌入在度量空间中的网络导出了一组连接概念。除了这些数学和文本指南外，我们还提出了一种用于网络图的统一图形表示法，以促进对网络属性的直观理解。具有代表性的网络模型示例演示了这些想法的实际用途。我们希望所提出的标准化将有助于在计算神经科学中对神经元网络连接进行明确的描述和可重复的实现。
