When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to threatening stimuli in ∼250 milliseconds and, in simple environments, use spatial memory to quickly escape to shelter. Natural habitats, however, often offer multiple routes to safety that animals must identify and choose from. This is challenging because although rodents can learn to navigate complex mazes, learning the value of different routes through trial and error during escape could be deadly. Here, we investigated how mice learn to choose between different escape routes. Using environments with paths to shelter of varying length and geometry, we find that mice prefer options that minimize path distance and angle relative to the shelter. This strategy is already present during the first threat encounter and after only ∼10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic assigns survival value to each path after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modeling shows that model-based reinforcement learning agents replicate the observed behavior in environments where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. The results further suggest that integrating prior knowledge acquired through evolution with knowledge learned from experience supports adaptation to changing environments and minimizes the need for trial and error when the errors are costly.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Innate heuristics and fast learning support escape route selection in mice


    Beteiligte:
    Claudi, Federico (Autor:in) / Campagner, Dario (Autor:in) / Branco, Tiago (Autor:in)

    Erscheinungsdatum :

    2022-05-25


    Anmerkungen:

    Current Biology , 32 (13) 2980-2987.e5. (2022)


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning

    Zheng, Zhi / Yao, Shunyu / Li, Genghui et al. | IEEE | 2024


    METHOD FOR PRODUCING AN ESCAPE ROUTE MARKING AND AN ESCAPE ROUTE MARKING

    SUTTER WOLFGANG | Europäisches Patentamt | 2018

    Freier Zugriff

    Method for producing an escape route marking for airplanes, and said escape route marking

    BIEHL TORBEN / LIEROW HANS-CHRISTIAN | Europäisches Patentamt | 2017

    Freier Zugriff

    ESCAPE ROUTE MARKING IN VEHICLES

    SUTTER WOLFGANG | Europäisches Patentamt | 2023

    Freier Zugriff

    Method for producing an escape route marking for air planes, and said escape route marking

    BIEHL TORBEN / LIEROW HANS-CHRISTIAN | Europäisches Patentamt | 2015

    Freier Zugriff