Literature DB >> 25601577

A 1-night operant learning task without food-restriction differentiates among mouse strains in an automated home-cage environment.

Esther Remmelink1, Maarten Loos2, Bastijn Koopmans3, Emmeke Aarts4, Sophie van der Sluis5, August B Smit, Matthijs Verhage6.   

Abstract

Individuals are able to change their behavior based on its consequences, a process involving instrumental learning. Studying instrumental learning in mice can provide new insights in this elementary aspect of cognition. Conventional appetitive operant learning tasks that facilitate the study of this form of learning in mice, as well as more complex operant paradigms, require labor-intensive handling and food deprivation to motivate the animals. Here, we describe a 1-night operant learning protocol that exploits the advantages of automated home-cage testing and circumvents the interfering effects of food restriction. The task builds on behavior that is part of the spontaneous exploratory repertoire during the days before the task. We compared the behavior of C57BL/6J, BALB/cJ and DBA/2J mice and found various differences in behavior during this task, but no differences in learning curves. BALB/cJ mice showed the largest instrumental learning response, providing a superior dynamic range and statistical power to study instrumental learning by using this protocol. Insights gained with this home-cage-based learning protocol without food restriction will be valuable for the development of other, more complex, cognitive tasks in automated home-cages.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Automated home-cage; Instrumental learning; Mice; Operant conditioning; PhenoTyper

Mesh:

Year:  2015        PMID: 25601577     DOI: 10.1016/j.bbr.2015.01.020

Source DB:  PubMed          Journal:  Behav Brain Res        ISSN: 0166-4328            Impact factor:   3.332


  6 in total

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Authors:  Shimin Zou; Chengyu Tony Li
Journal:  Neurosci Bull       Date:  2019-04-11       Impact factor: 5.203

2.  Simultaneous assessment of cognitive function, circadian rhythm, and spontaneous activity in aging mice.

Authors:  Sreemathi Logan; Daniel Owen; Sixia Chen; Wei-Jen Chen; Zoltan Ungvari; Julie Farley; Anna Csiszar; Amanda Sharpe; Maarten Loos; Bastijn Koopmans; Arlan Richardson; William E Sonntag
Journal:  Geroscience       Date:  2018-04-24       Impact factor: 7.713

3.  Measuring discrimination- and reversal learning in mouse models within 4 days and without prior food deprivation.

Authors:  Esther Remmelink; August B Smit; Matthijs Verhage; Maarten Loos
Journal:  Learn Mem       Date:  2016-10-17       Impact factor: 2.460

4.  High-Throughput Automated Olfactory Phenotyping of Group-Housed Mice.

Authors:  Janine K Reinert; Andreas T Schaefer; Thomas Kuner
Journal:  Front Behav Neurosci       Date:  2019-12-17       Impact factor: 3.558

5.  Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives.

Authors:  Fabrizio Grieco; Briana J Bernstein; Barbara Biemans; Lior Bikovski; C Joseph Burnett; Jesse D Cushman; Elsbeth A van Dam; Sydney A Fry; Bar Richmond-Hacham; Judith R Homberg; Martien J H Kas; Helmut W Kessels; Bastijn Koopmans; Michael J Krashes; Vaishnav Krishnan; Sreemathi Logan; Maarten Loos; Katharine E McCann; Qendresa Parduzi; Chaim G Pick; Thomas D Prevot; Gernot Riedel; Lianne Robinson; Mina Sadighi; August B Smit; William Sonntag; Reinko F Roelofs; Ruud A J Tegelenbosch; Lucas P J J Noldus
Journal:  Front Behav Neurosci       Date:  2021-09-24       Impact factor: 3.617

6.  High-Throughput Automatic Training System for Odor-Based Learned Behaviors in Head-Fixed Mice.

Authors:  Zhe Han; Xiaoxing Zhang; Jia Zhu; Yulei Chen; Chengyu T Li
Journal:  Front Neural Circuits       Date:  2018-02-13       Impact factor: 3.492

  6 in total

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