The roles of supervised machine learning in systems neuroscience.

Affiliation

Department of Bioengineering, University of Pennsylvania, United States. Electronic address: [Email]

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.

Keywords

Deep learning,Machine learning,Modeling,Neural activity,Neuroanatomy,Supervised learning,Systems neuroscience,

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