Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality
| Autor: | Takuya Isomura, Hideaki Shimazaki, Tazu Aoki, Hisaya Kakinuma, Hitoshi Okamoto, Tanvir Islam, Chi Chung Alan Fung, Tomoki Fukai, Makio Torigoe |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Intravital Microscopy
Computer science media_common.quotation_subject Science Decision Internal model General Physics and Astronomy Neocortex Virtual reality General Biochemistry Genetics and Molecular Biology Article Stereotaxic Techniques Reward Avoidance Learning Animals Zebrafish media_common Neurons Motivation Multidisciplinary Artificial neural network biology Behavior Animal business.industry Virtual Reality General Chemistry Maximization biology.organism_classification Surprise Microscopy Fluorescence Multiphoton Action (philosophy) Stereotaxic technique Artificial intelligence Neural Networks Computer business Photic Stimulation |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-21 (2021) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | 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. Using a closed-loop virtual reality system for fish, the authors show that zebrafish are capable of assigning rules to the scenery they see, and of generating a state prediction error by comparing reality with a prediction derived from an internal model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink