CaltechAUTHORS
  A Caltech Library Service

Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration

Rosenberg, Matthew and Zhang, Tony and Perona, Pietro and Meister, Markus (2021) Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210119-130811113

[img] PDF - Submitted Version
Creative Commons Attribution.

4Mb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20210119-130811113

Abstract

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 quickly discovers the location of a reward in the maze 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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2021.01.14.426746DOIDiscussion Paper
https://github.com/markusmeister/Rosenberg-2021-RepositoryRelated ItemCode
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Meister, Markus0000-0003-2136-6506
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. This version posted January 15, 2021. Data availability: All data and code needed to reproduce the figures and quoted results are available in this public repository: https://github.com/markusmeister/Rosenberg-2021-Repository. This work was supported by the Simons Collaboration on the Global Brain (grant 543015 to MM and 543025 to PP), by NSF award 1564330 to PP, and by a gift from Google to PP. Author contributions: Conception of the study MR, TZ, PP, MM; Data collection MR, TZ; Analysis and interpretation MR, TZ, PP, MM; Drafting the manuscript MM; Revision and approval MR, TZ, PP, MM. The authors declare no competing interests. Data and code availability: Data and code will be available in a public repository following acceptance of the manuscript.
Funders:
Funding AgencyGrant Number
Simons Foundation543015
Simons Foundation543025
NSFIIS-1564330
GoogleUNSPECIFIED
Record Number:CaltechAUTHORS:20210119-130811113
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210119-130811113
Official Citation:Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration. Matthew Rosenberg, Tony Zhang, Pietro Perona, Markus Meister. bioRxiv 2021.01.14.426746; doi: https://doi.org/10.1101/2021.01.14.426746
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107545
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Jan 2021 21:34
Last Modified:19 Jan 2021 21:34

Repository Staff Only: item control page