Literature DB >> 29037586

Successive and discrete spaced conditioning in active avoidance learning in young and aged zebrafish.

Peng Yang1, Riki Kajiwara1, Ayako Tonoki1, Motoyuki Itoh2.   

Abstract

We designed an automated device to study active avoidance learning abilities of zebrafish. Open source tools were used for the device control, statistical computing, and graphic outputs of data. Using the system, we developed active avoidance tests to examine the effects of trial spacing and aging on learning. Seven-month-old fish showed stronger avoidance behavior as measured by color preference index with discrete spaced training as compared to successive spaced training. Fifteen-month-old fish showed a similar trend, but with reduced cognitive abilities compared with 7-month-old fish. Further, in 7-month-old fish, an increase in learning ability during trials was observed with discrete, but not successive, spaced training. In contrast, 15-month-old fish did not show increase in learning ability during trials. Therefore, these data suggest that discrete spacing is more effective for learning than successive spacing, with the zebrafish active avoidance paradigm, and that the time course analysis of active avoidance using discrete spaced training is useful to detect age-related learning impairment.
Copyright © 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

Entities:  

Keywords:  Aging; Automated learning device; Learning; Zebrafish

Mesh:

Year:  2017        PMID: 29037586     DOI: 10.1016/j.neures.2017.10.005

Source DB:  PubMed          Journal:  Neurosci Res        ISSN: 0168-0102            Impact factor:   3.304


  2 in total

1.  Spatial Learning Promotes Adult Neurogenesis in Specific Regions of the Zebrafish Pallium.

Authors:  Laura S Mazzitelli-Fuentes; Fernanda R Román; Julio R Castillo Elías; Emilia B Deleglise; Lucas A Mongiat
Journal:  Front Cell Dev Biol       Date:  2022-05-11

2.  Zebrafish behavior feature recognition using three-dimensional tracking and machine learning.

Authors:  Peng Yang; Hiro Takahashi; Masataka Murase; Motoyuki Itoh
Journal:  Sci Rep       Date:  2021-06-29       Impact factor: 4.379

  2 in total

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