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Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures

机译:神经示意图作为大型神经网络结构的统一形式化图形表示

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摘要

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.
机译:神经科学研究的主要成果之一是神经网络结构(NNS)模型。这些模型的描述通常由文本,图形和印刷媒体中其他视觉信息交流手段的非标准化组合组成。然而,由于神经科学本质上是一个跨学科领域,因此需要一种统一表示NNS模型的标准化方法。尽管最近已经开发了以文本形式的此类模型的一般性描述,但迄今为止,尚不存在以示意方式表达它们的形式化方法。因此,在本文中,我们将神经原理图作为一种概念受到其他学科的类似方法的启发,用于表示所述结构的通用二维表示形式。一般介绍NNS之后,将对NNS模型的当前可视化集合进行回顾和分析,以了解它们传达了哪些信息以及如何呈现其元素。然后,该分析允许定义常规项目和符号,以在二维平面上将这些模型一致地表示为神经示意图。我们将说明已达成共识的标准可以在转换为神经示意图的采样图上产生的可能性,以及用于大规模NNS设计和建模的示例应用程序。

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