Analysis of Operant Self-administration Behaviors with Supervised Machine Learning: Protocol for Video Acquisition and Pose Estimation Analysis Using DeepLabCut and Simple Behavioral Analysis.

Journal: eNeuro
PMID: 39774006

Abstract

The use of supervised machine learning to approximate poses in video recordings allows for rapid and efficient analysis of complex behavioral profiles. Currently, there are limited protocols for automated analysis of operant self-administration behavior. We provide a methodology to (1) obtain videos of training sessions via Raspberry Pi microcomputers or GoPro cameras, (2) obtain pose estimation data using the supervised machine learning software packages DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA) with a local high-performance computer cluster, (3) compare standard Med-PC lever response versus quadrant time data generated from pose estimation regions of interest, and (4) generate predictive behavioral classifiers. Overall, we demonstrate proof of concept to use pose estimation outputs from DLC to both generate quadrant time results and obtain behavioral classifiers from SimBA during operant training phases.

Authors

  • Leo F Pereira Sanabria
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
  • Luciano S Voutour
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
  • Victoria J Kaufman
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
  • Christopher A Reeves
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
  • Aneesh S Bal
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823.
  • Fidel Maureira
    Department of Earth and Environmental Sciences, Michigan State University, East Lansing, Michigan 48823.
  • Amy A Arguello
    Department of Psychology, Michigan State University, East Lansing, Michigan 48823 arguell5@msu.edu.

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