Adaptive Behaviour Research Group, Department of Psychology, University of Sheffield Sheffield, UK.
Front Neuroinform. 2009 Mar 9;3:6. doi: 10.3389/neuro.11.006.2009. eCollection 2009.
Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large-scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.
计算神经科学正逐渐超越对单个神经元或神经网络的建模，转而考虑对多个模型的整合，这些模型通常由不同的研究小组构建。我们报告了最近在海马体形成、基底神经节和物理环境模型方面的初步技术整合情况，以及可视化工具，这是在建模工具链中使用 Python 的案例研究。我们在此不呈现新的建模结果。该架构包含漏电积分器和率编码神经元、具有碰撞检测和触觉传感器的 3D 环境、3D 图形和 2D 图。我们发现 Python 是一个灵活的平台，提供了显著减少开发时间的优势，而执行时间没有相应的显著增加。我们通过在各种替代语言和编码风格中实现模型的一部分来阐明这一点，并比较它们的执行时间。对于非常大规模的系统集成，可能需要与其他语言进行通信和并行执行，我们使用 BRAHMS 框架的 Python 绑定对此进行了演示。
