Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki 00029, Finland, and School of Informatics, Computing and Engineering, Indiana University Bloomington, IN 47408, U.S.A.
Clinical Neurosciences, Neurology, University of Helsinki and Helsinki University Hospital, Helsinki 00029, Finland
Neural Comput. 2019 Jun;31(6):1048-1065. doi: 10.1162/neco_a_01120. Epub 2018 Aug 27.
Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model. Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices. Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues ( 2015 ). Our results indicate that the framework can efficiently perform simulations using Python, C , and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C devices inherited the single core limitation of Brian2. The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.
模拟大脑皮层需要结合广泛的领域特定知识和高效的软件。然而，当生物系统的复杂性与软件的复杂性相结合时，编码错误的可能性会增加，从而减缓模型调整的速度。此外，很少有生命科学家熟悉软件工程，他们会受益于生物模型的高级抽象形式的简洁性。我们的主要目标是为个人计算机构建一个可扩展的皮层模拟框架。我们从软件中分离出了领域特定知识的可调节部分。接下来，我们设计了一个框架，该框架可以从逗号分隔值文件中读取模型参数，并为 Brian2 模型模拟创建必要的代码。这种分离允许快速探索复杂的皮层电路，同时降低编码错误的可能性，并自动使用高效的硬件设备。接下来，我们在 Markram 及其同事提出的简化新皮层微电路版本上测试了该系统（2015）。我们的结果表明，该框架可以使用 Python、C 和 GPU 设备高效地执行模拟。最有效的设备因计算机硬件以及模拟系统的持续时间和规模而异。尽管有一个软件层，但 Brian2 的速度得以保留。然而，Python 和 C 设备继承了 Brian2 的单核限制。CxSystem 框架支持在个人计算机上探索复杂模型，因此有可能促进对皮层网络和系统的研究。
