Aleksin Sergey G, Zheng Kaiyu, Rusakov Dmitri A, Savtchenko Leonid P
AMC Bridge LLC, Waltham MA, United States of America and Dnipro, Ukraine.
UCL Institute of Neurology, University College London, London WC1N 3BG, United Kingdom.
PLoS Comput Biol. 2017 Mar 31;13(3):e1005467. doi: 10.1371/journal.pcbi.1005467. eCollection 2017 Mar.
Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handling of which requires specific programing skills. Here we put forward a newly developed simulation environment ARACHNE: it enables an investigator to build and explore cellular networks of arbitrary biophysical and architectural complexity using the logic of NEURON and a simple interface on a local computer or a mobile device. The interface can control, through the internet, an optimized computational kernel installed on a remote computer cluster. ARACHNE can combine neuronal (wired) and astroglial (extracellular volume-transmission driven) network types and adopt realistic cell models from the NEURON library. The program and documentation (current version) are available at GitHub repository https://github.com/LeonidSavtchenko/Arachne under the MIT License (MIT).
迄今为止,创建和运行逼真的神经网络模型一直是计算专业人员而非实验神经科学家的任务。这主要是因为此类网络通常需要大量计算资源,而处理这些资源需要特定的编程技能。在此,我们提出了一个新开发的模拟环境ARACHNE:它使研究人员能够使用NEURON的逻辑以及本地计算机或移动设备上的简单界面,构建和探索具有任意生物物理和结构复杂性的细胞网络。该界面可以通过互联网控制安装在远程计算机集群上的优化计算内核。ARACHNE可以结合神经元(有线)和星形胶质细胞(细胞外容积传输驱动)网络类型,并采用NEURON库中的逼真细胞模型。该程序和文档(当前版本)可在GitHub仓库https://github.com/LeonidSavtchenko/Arachne上获取,遵循麻省理工学院许可协议(MIT)。