Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Goodwin, Nastacia L.; Nilsson, Simon R.O.; Golden, Sam A.

doi:10.1007/s00213-020-05577-x

Review
Published: 09 July 2020

Rage Against the Machine: Advancing the study of aggression ethology via machine learning.

Nastacia L. Goodwin^1,2^na1,
Simon R.O. Nilsson¹^na1 &
Sam A. Golden ORCID: orcid.org/0000-0002-2104-2272^1,2,3

Psychopharmacology volume 237, pages 2569–2588 (2020)Cite this article

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Abstract

Rationale

Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods.

Objectives

In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies.

Key results

Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species.

Discussion and conclusions

Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.

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Acknowledgments

We thank Briana Smith, Liana Bloom, Sophia Hwang, and Jia Jie Choong for their thoughtful assistance in discussing the manuscript.

Funding

The research was supported NIDA 4R00DA045662-02 (SAG), NIDA P30 DA048736 (SRON and SAG)), NARSAD Young Investigator Award 27082 (SAG), and NIDA T32 5T32NS099578-04 (NLG).

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Nastacia L. Goodwin and Simon R.O. Nilsson contributed equally to this work.

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Department of Biological Structure, University of Washington, Seattle, WA, USA
Nastacia L. Goodwin, Simon R.O. Nilsson & Sam A. Golden
Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA
Nastacia L. Goodwin & Sam A. Golden
Center of Excellence in Neurobiology of Addiction, Pain, and Emotion (NAPE), University of Washington, Seattle, WA, USA
Sam A. Golden

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Nastacia L. Goodwin
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Simon R.O. Nilsson
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Sam A. Golden
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Goodwin, N.L., Nilsson, S.R. & Golden, S.A. Rage Against the Machine: Advancing the study of aggression ethology via machine learning.. Psychopharmacology 237, 2569–2588 (2020). https://doi.org/10.1007/s00213-020-05577-x

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Received: 07 November 2019
Accepted: 01 June 2020
Published: 09 July 2020
Issue Date: September 2020
DOI: https://doi.org/10.1007/s00213-020-05577-x

Keywords

Aggression
Machine learning
Mice
Motivation
Reward