  The uninformative ordering of artificial neurons in Deep Neural Networks
complicates visualizing activations in deeper layers. This is one reason why
the internal structure of such models is very unintuitive. In neuroscience,
activity of real brains can be visualized by highlighting active regions.
Inspired by those techniques, we train a convolutional speech recognition
model, where filters are arranged in a 2D grid and neighboring filters are
similar to each other. We show, how those topographic filter maps visualize
artificial neuron activations more intuitively. Moreover, we investigate,
whether this causes phoneme-responsive neurons to be grouped in certain regions
of the topographic map.
