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Review
. 2019 Apr:175:126-137.
doi: 10.1016/j.pneurobio.2019.01.008. Epub 2019 Feb 7.

The roles of supervised machine learning in systems neuroscience

Joshua I Glaser  1 , Ari S Benjamin  2 , Roozbeh Farhoodi  3 , Konrad P Kording  4
Affiliations
Review

The roles of supervised machine learning in systems neuroscience

Joshua I Glaser et al. Prog Neurobiol. 2019 Apr.

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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Figures

Figure 1:
Figure 1:. Growth of Machine Learning in Neuroscience.
Here we plot the proportion of neuroscience papers that have used ML over the last two decades. That is, we calculate the number of papers involving both neuroscience and machine learning, normalized by the total number of neuroscience papers. Neuroscience papers were identified using a search for “neuroscience” on Semantic Scholar. Papers involving neuroscience and machine learning were identified with a search for “machine learning” and “neuroscience” on Semantic Scholar.
Figure 2:
Figure 2:. Examples of the four roles of supervised machine learning in neuroscience.
1 - ML can solve engineering problems. For example, it can help researchers control a prosthetic limb using brain activity. 2 - ML can identify predictive variables. For example, by using MRI data, we can identify which brain regions are most predictive for diagnosing Alzheimer’s disease (Lebedev et al. 2014). 3 - ML can benchmark simple models. For example, we can compare the predictive performance of the simple “population vector” model of how neural activity relates to movement (Georgopoulos, Schwartz, and Kettner 1986) to a ML benchmark (e.g. an RNN). 4 - ML can serve as a model of the brain. For example, researchers have studied how neurons in the visual pathway correspond to units in an artificial network that is trained to classify images (Yamins and DiCarlo 2016).
None
See this image and copyright information in PMC

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