  Neuroscientists apply a range of common analysis tools to recorded neural
activity in order to glean insights into how neural circuits implement
computations. Despite the fact that these tools shape the progress of the field
as a whole, we have little empirical evidence that they are effective at
quickly identifying the phenomena of interest. Here I argue that these tools
should be explicitly tested and that artificial neural networks (ANNs) are an
appropriate testing grounds for them. The recent resurgence of the use of ANNs
as models of everything from perception to memory to motor control stems from a
rough similarity between artificial and biological neural networks and the
ability to train these networks to perform complex high-dimensional tasks.
These properties, combined with the ability to perfectly observe and manipulate
these systems, makes them well-suited for vetting the tools of systems and
cognitive neuroscience. I provide here both a roadmap for performing this
testing and a list of tools that are suitable to be tested on ANNs. Using ANNs
to reflect on the extent to which these tools provide a productive
understanding of neural systems -- and on exactly what understanding should
mean here -- has the potential to expedite progress in the study of the brain.
