Mice in a labyrinth show rapid learning, sudden insight, and efficient exploration.
behavior
cognitive map
computational biology
decision-making
few-shot learning
mouse
navigation
neuroscience
predictive models
systems biology
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
01 07 2021
01 07 2021
Historique:
received:
31
12
2020
accepted:
30
06
2021
pubmed:
2
7
2021
medline:
21
10
2021
entrez:
1
7
2021
Statut:
epublish
Résumé
Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.
Identifiants
pubmed: 34196271
doi: 10.7554/eLife.66175
pii: 66175
pmc: PMC8294850
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NINDS NIH HHS
ID : T32 NS105595
Pays : United States
Informations de copyright
© 2021, Rosenberg et al.
Déclaration de conflit d'intérêts
MR, TZ, PP No competing interests declared, MM Reviewing editor, eLife
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