The computational neuroscience discipline roughly divides into two subfields. A first, which may be called theoretical neuroscience focuses on principled approaches towards arriving at meaningful models of the nervous system. This field contains many aspects of mathematical neuroscience which employs mathematical techniques to arrive at models. Models in theoretical neuroscience are often aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, all the way up to behavior. These computational models frame hypotheses that can often be directly tested by biological or psychological experiments. A second subfield, which is often called neural data science focuses on approaches towards making sense of the progressively larger datasets in neuroscience. This may include the processing of electrophysiological or imaging data, the fitting of models to data, and the comparison of models. These two subfields are highly synergistic and many papers draw from both traditions.