  Over the last several years, the use of machine learning (ML) in neuroscience
has been rapidly increasing. Here, we review ML's contributions, both realized
and potential, across several areas of systems neuroscience. We describe four
primary roles of ML within neuroscience: 1) creating solutions to engineering
problems, 2) identifying predictive variables, 3) setting benchmarks for simple
models of the brain, and 4) serving itself as a model for the brain. The
breadth and ease of its applicability suggests that machine learning should be
in the toolbox of most systems neuroscientists.
