AUTHOR=Ehrlich Matthias , Schüffny René TITLE=Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures JOURNAL=Frontiers in Neuroinformatics VOLUME=Volume 7 - 2013 YEAR=2013 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2013.00022 DOI=10.3389/fninf.2013.00022 ISSN=1662-5196 ABSTRACT=

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.