Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. However, the complexity of DNNs makes it difficult to understand how they solve their learned task. To improve the explainability of DNNs, we adapt methods from neuroscience that analyze complex and opaque systems. Here, we draw inspiration from how neuroscience uses topographic maps to visualize brain activity. To also visualize activations of neurons in DNNs as topographic maps, we research techniques to layout the neurons in a two-dimensional space such that neurons of similar activity are in the vicinity of each other. In this work, we introduce and compare methods to obtain a topographic layout of neurons in a DNN layer. Moreover, we demonstrate how to use topographic activation maps to identify errors or encoded biases and to visualize training processes. Our novel visualization technique improves the transparency of DNN-based decision-making systems and is interpretable without expert knowledge in Machine Learning.

翻译：利用深度神经网络（DNN）的机器学习已成为解决各领域应用任务的成功工具。然而，DNN的复杂性使得理解其学习任务的解决过程变得困难。为提升DNN的可解释性，我们借鉴神经科学中分析复杂且不透明系统的方法，从神经科学利用地形图可视化脑活动的原理中汲取灵感。为将DNN中神经元的激活同样以地形图形式呈现，我们研究在二维空间中对神经元进行布局的技术，使相似活动的神经元彼此邻近。本文提出并比较了在DNN层中获取神经元地形布局的方法，同时展示了如何利用地形激活图识别错误或编码偏差，以及可视化训练过程。这一新型可视化技术提升了基于DNN的决策系统的透明度，且无需机器学习专业知识即可解读。