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An Open-Source Joystick Platform for Investigating Forelimb Motor Control, Auditory-Motor Integration, and

Ivan Linares-García1, Evan A Iliakis2, Sofia E Juliani3

  • 1Department of Cell Biology and Neuroscience, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854 ivan.linares@rutgers.edu david.margolis@rutgers.edu.

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Summary

Researchers developed an open-source platform for mouse behavioral training, capturing detailed motor control and decision-making data. This system enhances studies on sensorimotor integration and choice behavior.

Keywords:
auditory-motorjoysticklearningmouse behavioropen-sourcevalue-based

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Area of Science:

  • Neuroscience
  • Behavioral Science
  • Robotics

Background:

  • Investigating neural mechanisms of motor control necessitates detailed behavioral data.
  • Existing methods often lack comprehensive readouts of both choice and execution.
  • Capturing kinematic and categorical information is crucial for understanding decision-making.

Purpose of the Study:

  • To present an open-source platform for behavioral training in head-fixed mice.
  • To enable detailed analysis of motor control and decision-making processes.
  • To provide a flexible and accessible tool for neuroscience research.

Main Methods:

  • Developed a behavioral training platform using a forelimb-based joystick, sound system, lick sensor, and water dispenser.
  • Implemented two paradigms: auditory-motor discrimination and value-based decision-making.
  • Collected extensive kinematic data (speed, displacement, trajectory similarity, tortuosity, vigor).

Main Results:

  • The platform successfully trained mice in auditory-motor and value-based decision tasks.
  • Rich kinematic parameters provided insights into learning and decision processes.
  • Movement vigor and trajectory analysis revealed nuances in motor execution.

Conclusions:

  • The open-source platform offers a versatile and advanced tool for studying sensorimotor integration and choice behavior in mice.
  • The detailed kinematic data complements categorical choice information for a richer understanding of neural control.
  • This resource facilitates adoption by researchers investigating motor control and decision-making mechanisms.