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NeuroscienceJournal of Neurophysiology
Peer-reviewed journal article

A platform for semi-automated voluntary training of common marmosets for behavioral neuroscience

Jeffrey D. WalkeriD, Friederice Pirschel, Nicholas J. Gidmark, Jason N. MacLeaniD, Nicholas G. HatsopoulosiD

Abstract

In most cases, behavioral neuroscience studies of the common marmoset employ adaptations of well-established methods used with macaque monkeys. However, in most cases these approaches do not readily generalize to marmosets indicating a need for alternatives. Here we present the development of one such alternate: a platform for semi-automated, voluntary in-home cage behavioral training that allows for the study of naturalistic behaviors. We describe the design and production of a modular behavioral training apparatus using CAD software and digital fabrication. We demonstrate that this apparatus permits voluntary behavioral training and data collection throughout the marmosets waking hours with little experimenter intervention. Further we demonstrate the use of this apparatus to reconstruct the kinematics of the marmosets upper limb movement during natural foraging behavior.\n\nNEW AND NOTEWORTHYThe study of marmosets in neuroscience has grown rapidly and this model organism presents challenges that are unique to this primate species. Here we address those challenges with an innovative platform for semi-automated and voluntary training of common marmosets. The platform allows marmosets to train throughout their waking hours with little to no experimenter intervention. We describe the use of this platform to capture the kinematics of the upper limb during natural foraging behavior and to expand the opportunities for behavioral training beyond the limits of traditional behavioral training sessions. The platform is flexible and can be easily extended to incorporate other motor tasks (e.g. visually cued reaching or manipulandum based tasks) using CAD models and digital fabrication.

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02 /Specification & Classification

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Citations
12
FWCI
3.00
Field percentile
top 9%

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