Explaining neural network models is a challenging task that remains unsolved
in its entirety to this day. This is especially true for high dimensional and
complex data. With the present work, we introduce two notions for conceptual
views of a neural network, specifically a many-valued and a symbolic view. Both
provide novel analysis methods to enable a human AI analyst to grasp deeper
insights into the knowledge that is captured by the neurons of a network. We
test the conceptual expressivity of our novel views through different
experiments on the ImageNet and Fruit-360 data sets. Furthermore, we show to
which extent the views allow to quantify the conceptual similarity of different
learning architectures. Finally, we demonstrate how conceptual views can be
applied for abductive learning of human comprehensible rules from neurons. In
summary, with our work, we contribute to the most relevant task of globally
explaining neural networks models.

本研究介绍了两种神经网络的概念视图，即多值视图和符号视图，并通过在 ImageNet 和 Fruit-360 数据集上的实验测试了其概念表达能力。此外，论文还展示了这些视图可以量化不同学习架构之间的概念相似度，并演示了如何将概念视图应用于从神经元中归纳学习人类可理解的规则，从而为全局解释神经网络模型做出贡献。