Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components.
Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components.
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DOI：
10.3389/neuro.11.008.2009
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发表时间：
2009
影响因子：
3.5
通讯作者：
Bednar JA
中科院分区：
文献类型：
--
作者：
Bednar JA
许多神经区域被布置为二维地形图，例如哺乳动物视觉皮层中的视网膜图。计算模拟导致了关于皮质形态如何发展和功能的宝贵见解，但缺乏适当的工具阻碍了进一步的进步。在细节级别上桥接的桥梁特别困难，因为模拟器通常会达到特定水平，而模拟器之间的接口一直是主要的技术挑战。在本文中，我们表明，基于Python的地形图模拟器使构建跨级分析的系统变得直接，并为评估和比较其他模拟器中实现的模型提供了一个共同的框架。这些结果依赖于设计地形图的通用抽象，以及用于许多模拟器的Python界面。特别是，我们提出了一个详细的通用示例，说明如何仅使用十二条Python代码将外部尖峰Pynn/Nest Simulation作为地形式组件包裹起来，从而可以使用任何广泛的输入呈现，分析，分析，分析，分析，并绘制地形图的工具。其他示例显示了如何轻松与其他类型的模拟器中的模型交互。外部模拟地形图的研究人员应考虑使用地形图的分析工具（例如偏好图，接受场或调谐曲线测量）来始终如一地比较结果，并用于在不同级别的模型中进行比较。这种无缝的互操作性将有助于神经科学家和计算科学家共同努力，了解地形图中的神经元如何组织和运行。
Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate.