The roles of supervised machine learning in systems neuroscience
Progress in neurobiology, 2019•Elsevier
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 …
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 …
Abstract
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.
Elsevier