Sutton Nate M, Ascoli Giorgio A
Department of Bioengineering, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA).
Interdepartmental Neuroscience Program, 4400 University Drive, George Mason University, Fairfax, Virginia, 22030 (USA).
Cogn Syst Res. 2021 Dec;70:80-92. doi: 10.1016/j.cogsys.2021.07.008. Epub 2021 Jul 31.
Computational modeling has contributed to hippocampal research in a wide variety of ways and through a large diversity of approaches, reflecting the many advanced cognitive roles of this brain region. The intensively studied neuron type circuitry of the hippocampus is a particularly conducive substrate for spiking neural models. Here we present an online knowledge base of spiking neural network simulations of hippocampal functions. First, we overview theories involving the hippocampal formation in subjects such as spatial representation, learning, and memory. Then we describe an original literature mining process to organize published reports in various key aspects, including: (i) subject area (e.g., navigation, pattern completion, epilepsy); (ii) level of modeling detail (Hodgkin-Huxley, integrate-and-fire, etc.); and (iii) theoretical framework (attractor dynamics, oscillatory interference, self-organizing maps, and others). Moreover, every peer-reviewed publication is also annotated to indicate the specific neuron types represented in the network simulation, establishing a direct link with the Hippocampome.org portal. The web interface of the knowledge base enables dynamic content browsing and advanced searches, and consistently presents evidence supporting every annotation. Moreover, users are given access to several types of statistical reports about the collection, a selection of which is summarized in this paper. This open access resource thus provides an interactive platform to survey spiking neural network models of hippocampal functions, compare available computational methods, and foster ideas for suitable new directions of research.
计算建模通过各种各样的方式和途径，对海马体研究做出了贡献，这反映了该脑区众多高级认知功能。海马体中经过深入研究的神经元类型电路，是脉冲神经网络模型特别有利的基础。在此，我们展示了一个关于海马体功能的脉冲神经网络模拟的在线知识库。首先，我们概述了涉及海马体形成的理论，这些理论存在于诸如空间表征、学习和记忆等主题中。然后我们描述了一个原创的文献挖掘过程，以在各个关键方面组织已发表的报告，包括：（i）主题领域（例如导航、模式完成、癫痫）；（ii）建模细节水平（霍奇金 - 赫胥黎模型、积分发放模型等）；以及（iii）理论框架（吸引子动力学、振荡干扰、自组织映射等）。此外，每篇经过同行评审的出版物也都有注释，以表明网络模拟中所代表的特定神经元类型，从而与Hippocampome.org门户网站建立直接联系。知识库的网络界面支持动态内容浏览和高级搜索，并始终呈现支持每个注释的证据。此外，用户可以访问有关该集合的几种类型的统计报告，本文总结了其中一部分。因此，这个开放获取资源提供了一个交互式平台，用于审视海马体功能的脉冲神经网络模型、比较可用的计算方法，并促进适合新研究方向的思路。
