  Connecting neural activity to function is a common aim in neuroscience. How
to define and conceptualize function, however, can vary. Here I focus on
grounding this goal in the specific question of how a given change in behavior
is produced by a change in neural circuits or activity. Artificial neural
network models offer a particularly fruitful format for tackling such questions
because they use neural mechanisms to perform complex transformations and
produce appropriate behavior. Therefore, they can be a means of causally
testing the extent to which a neural change can be responsible for an
experimentally observed behavioral change. Furthermore, because the field of
interpretability in artificial intelligence has similar aims, neuroscientists
can look to interpretability methods for new ways of identifying neural
features that drive performance and behaviors.
