A fully automated high-throughput training system for rodents.

Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled...

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Main Authors: Rajesh Poddar, Risa Kawai, Bence P Ölveczky
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3857823?pdf=render
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author Rajesh Poddar
Risa Kawai
Bence P Ölveczky
author_facet Rajesh Poddar
Risa Kawai
Bence P Ölveczky
author_sort Rajesh Poddar
collection DOAJ
description Addressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the implementation of predefined training protocols within the animal's home-cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. By enabling fully automated training of rodents in a home-cage setting this low-cost and modular system increases the utility of rodents for studying the neural underpinnings of a variety of complex behaviors.
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spelling doaj.art-2dc83fac274348fe916b72b1ba3bee852022-12-22T00:52:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8317110.1371/journal.pone.0083171A fully automated high-throughput training system for rodents.Rajesh PoddarRisa KawaiBence P ÖlveczkyAddressing the neural mechanisms underlying complex learned behaviors requires training animals in well-controlled tasks, an often time-consuming and labor-intensive process that can severely limit the feasibility of such studies. To overcome this constraint, we developed a fully computer-controlled general purpose system for high-throughput training of rodents. By standardizing and automating the implementation of predefined training protocols within the animal's home-cage our system dramatically reduces the efforts involved in animal training while also removing human errors and biases from the process. We deployed this system to train rats in a variety of sensorimotor tasks, achieving learning rates comparable to existing, but more laborious, methods. By incrementally and systematically increasing the difficulty of the task over weeks of training, rats were able to master motor tasks that, in complexity and structure, resemble ones used in primate studies of motor sequence learning. By enabling fully automated training of rodents in a home-cage setting this low-cost and modular system increases the utility of rodents for studying the neural underpinnings of a variety of complex behaviors.http://europepmc.org/articles/PMC3857823?pdf=render
spellingShingle Rajesh Poddar
Risa Kawai
Bence P Ölveczky
A fully automated high-throughput training system for rodents.
PLoS ONE
title A fully automated high-throughput training system for rodents.
title_full A fully automated high-throughput training system for rodents.
title_fullStr A fully automated high-throughput training system for rodents.
title_full_unstemmed A fully automated high-throughput training system for rodents.
title_short A fully automated high-throughput training system for rodents.
title_sort fully automated high throughput training system for rodents
url http://europepmc.org/articles/PMC3857823?pdf=render
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