Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the “mass” of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files, NeuroMorpho.org SWC files and plain ASCII files. Full 3D visualization and projections of the density to 1D and 2D manifolds are supported by the DVP. All routines are embedded within the visual programming IDE VRL-Studio for Java which allows the definition and rapid modification of analysis workflows.

中文翻译：

---

密度可视化管道：蜂窝和网络密度可视化和分析的工具

神经元分类是分析网络结构和量化神经元拓扑对信号处理的影响的重要组成部分。当前的量化和分类方法依赖于形态学投影到低维空间。本文提出了一种 3D 可视化和量化工具。密度可视化管道 (DVP) 计算、可视化和量化密度分布，即中间神经元的“质量”。我们使用 DVP 来表征和分类一组 GABA 能中间神经元。GABA 能中间神经元的分类对于一方面理解它们的各种功能，另一方面理解它们在新皮质中的普遍存在至关重要。基于这些指标，3D 密度图可视化和一维 x、y、z 子空间的投影显示了所研究细胞之间的明显区别。DVP 可以与神经元和网络行为的计算研究相结合，其中网络拓扑信息源自 DVP 信息。DVP 读取常见的神经形态学文件格式，例如 Neurolucida XML 文件、NeuroMorpho.org SWC 文件和纯 ASCII 文件。DVP 支持完整的 3D 可视化以及密度到 1D 和 2D 流形的投影。所有例程都嵌入到 Java 可视化编程 IDE VRL-Studio 中，允许定义和快速修改分析工作流程。