Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences As, Norway.
Front Neuroinform. 2010 Jan 29;3:39. doi: 10.3389/neuro.11.039.2009. eCollection 2010.
Complex ideas are best conveyed through well-designed illustrations. Up to now, computational neuroscientists have mostly relied on box-and-arrow diagrams of even complex neuronal networks, often using ad hoc notations with conflicting use of symbols from paper to paper. This significantly impedes the communication of ideas in neuronal network modeling. We present here Connectivity Pattern Tables (CPTs) as a clutter-free visualization of connectivity in large neuronal networks containing two-dimensional populations of neurons. CPTs can be generated automatically from the same script code used to create the actual network in the NEST simulator. Through aggregation, CPTs can be viewed at different levels, providing either full detail or summary information. We also provide the open source ConnPlotter tool as a means to create connectivity pattern tables.
复杂的想法最好通过精心设计的插图来传达。到目前为止，计算神经科学家主要依赖于甚至复杂神经网络的框和箭头图，这些图通常使用特定符号，符号在不同的论文中使用方式相互冲突。这极大地阻碍了神经元网络建模中思想的交流。我们在这里提出连接模式表 (CPT)，作为一种无杂乱的大型神经元网络连接可视化方法，其中包含二维神经元群体。CPT 可以从用于在 NEST 模拟器中创建实际网络的相同脚本代码自动生成。通过聚合，CPT 可以在不同级别查看，提供完整的细节或摘要信息。我们还提供了开源的 ConnPlotter 工具，作为创建连接模式表的一种手段。
