Highly-Parallel VLSI-Systems and Neuromorphic Circuits, Institute of Circuits and Systems, Technische Universität Dresden Dresden, Germany.
Front Neuroinform. 2013 Oct 24;7:22. doi: 10.3389/fninf.2013.00022. eCollection 2013.
One of the major outcomes of neuroscientific research are models of Neural Network Structures (NNSs). Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of NNSs is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing NNSs in general, a set of current visualizations of models of NNSs is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale NNSs.
神经科学研究的主要成果之一是神经网络结构 (NNS) 的模型。这些模型的描述通常由文本、图形和其他印刷媒体中的视觉信息交流手段的非标准化混合组成。然而，由于神经科学本质上是一个跨学科领域，因此需要一种标准化的方法来一致地表示 NNS 模型。虽然最近已经开发出了此类模型的文本形式的通用描述，但迄今为止还没有形式化的方法来示意性地表达它们。因此，在本文中，我们提出了神经图，这是受其他学科类似方法启发的概念，用于表示这些结构的通用二维表示。在一般介绍 NNS 之后，我们回顾和分析了当前对 NNS 模型的一些可视化方法，以了解它们传达的信息以及如何呈现其元素。然后，此分析允许定义通用项和符号，以便在二维平面上将这些模型一致地表示为神经图。我们将举例说明商定标准可以在转换为神经图的抽样图上产生的可能性，以及在设计和建模大规模 NNS 方面的应用示例。
