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EXPLORE: a novel deep learning-based analysis method for exploration behaviour in object recognition tests.

Victor Ibañez1,2, Laurens Bohlen3, Francesca Manuella4,5,6

  • 1Brain Research Institute, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland. victor.ibanez@uzh.ch.

Scientific Reports
|March 15, 2023
PubMed
Summary
This summary is machine-generated.

Manual scoring of rodent object exploration is time-consuming and variable. We developed EXPLORE, an open-source pipeline using deep learning, to accurately and efficiently analyze object recognition tests.

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Area of Science:

  • Neuroscience
  • Ethology
  • Computational Biology

Background:

  • Object recognition tests are crucial for assessing rodent memory.
  • Manual scoring of object exploration is labor-intensive and prone to variability.
  • Existing automated software often lacks precision for complex behaviors.

Purpose of the Study:

  • To develop an automated, accurate, and user-friendly pipeline for analyzing rodent object exploration.
  • To overcome the limitations of manual scoring and current tracking software.

Main Methods:

  • Development of "EXPLORE", an open-source pipeline utilizing a supervised convolutional neural network.
  • Training the network to extract image features and classify rodent behavior near objects.
  • Implementation of graphical user interfaces (GUIs) for end-to-end analysis.

Main Results:

  • EXPLORE achieves human-level accuracy in identifying and scoring object exploration.
  • Outperforms commercial software in precision, versatility, and time efficiency, especially in complex scenarios.
  • User-defined data labeling ensures precise analysis of specific interaction types.

Conclusions:

  • EXPLORE offers a precise, versatile, and efficient solution for analyzing object recognition tests.
  • Accelerates reproducible data analysis without requiring programming or deep learning expertise.
  • Facilitates more reliable assessment of memory function in rodents.