Summary of ssbd-repos-000223

Name
URL
DOI

Title
Adult zebrafish's telencephalic neural activity and behavior data during the GO/NOGO tasks in the virtual reality environment.
Description

These data are used in the following paper: Torigoe, M et al., Zebrafish capable of generating the prediction error showed improved active avoidance behavior. Nature Communications (2021).To elucidate the mechanisms of decision making, authors established a closed-loop virtual reality system for adult zebrafish. In the virtual space, zebrafish performed GO(active avoidance)/NOGO(passive avoidacne) tasks and authors captured the neural activity of the telencephalon during the tasks by 2-photon calcium imaging. These data includes calcium imaging movies, positions, fluorescence intensity and delf/f0 of all ROIs corresponding to cells extracted from the movies and behavioral data.

Submited Date
2022-02-14
Release Date
2022-03-23
Updated Date
-
License
Funding information
-
File formats
lsm, txt
Data size
581.7 GB

Organism
Danio rerio
Strain
TgBAC(camk2a:GAL4VP16); TgBAC(vglut2a:Gal4); Tg(UAS:G-CaMP7) zebrafih
Cell Line
-
Genes
-
Proteins
-

GO Molecular Function (MF)
-
GO Biological Process (BP)
-
GO Cellular Component (CC)
-
Study Type
-
Imaging Methods
-

Method Summary

Torigoe M, Islam T, Kakinuma H, Fung CCA, Isomura T, Shimazaki H, Aoki T, Fukai T, Okamoto H. Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality. Nat Commun. 2021 Sep 29;12(1):5712. doi: 10.1038/s41467-021-26010-7. PMID: 34588436; PMCID: PMC8481257.

Related paper(s)

Makio Torigoe, Tanvir Islam, Hisaya Kakinuma, Chi Chung Alan Fung, Takuya Isomura, Hideaki Shimazaki, Tazu Aoki, Tomoki Fukai, Hitoshi Okamoto (2021) Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality., Nature communications, Volume 12, Number 1, pp. 5712

Published in 2021 Sep 29 (Electronic publication in Sept. 29, 2021, midnight )

(Abstract) Animals make decisions under the principle of reward value maximization and surprise minimization. It is still unclear how these principles are represented in the brain and are reflected in behavior. We addressed this question using a closed-loop virtual reality system to train adult zebrafish for active avoidance. Analysis of the neural activity of the dorsal pallium during training revealed neural ensembles assigning rules to the colors of the surrounding walls. Additionally, one third of fish generated another ensemble that becomes activated only when the real perceived scenery shows discrepancy from the predicted favorable scenery. The fish with the latter ensemble escape more efficiently than the fish with the former ensembles alone, even though both fish have successfully learned to escape, consistent with the hypothesis that the latter ensemble guides zebrafish to take action to minimize this prediction error. Our results suggest that zebrafish can use both principles of goal-directed behavior, but with different behavioral consequences depending on the repertoire of the adopted principles.
(MeSH Terms)

Contact(s)
Makio Torigoe
Organization(s)
RIKEN , RIKEN Center for Brain Science , Laboratory for Neural Circuit Dynamics of Decision Making
Image Data Contributors
Hitoshi Okamoto, Hisaya Kakinuma, Tazu Aoki
Quantitative Data Contributors
Tanvir Islam

Download files
Download zipped files