1. Computational and Systems Biology
  2. Neuroscience
Download icon

Mice in a labyrinth exhibit rapid learning, sudden insight, and efficient exploration

  1. Matthew Rosenberg
  2. Tony Zhang
  3. Pietro Perona  Is a corresponding author
  4. Markus Meister  Is a corresponding author
  1. California Institute of Technology, United States
Research Article
  • Cited 2
  • Views 5,324
  • Annotations
Cite this article as: eLife 2021;10:e66175 doi: 10.7554/eLife.66175

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 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.

Data availability

The behavioral data and code that produced the figures are available in a public Github repository cited in the article https://github.com/markusmeister/Rosenberg-2021-Repository. We are also preparing a permanent institutional repository.

The following data sets were generated

Article and author information

Author details

  1. Matthew Rosenberg

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    No competing interests declared.
  2. Tony Zhang

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    No competing interests declared.
  3. Pietro Perona

    Computation and Neural Systems, California Institute of Technology, Pasadena, United States
    For correspondence
    perona@caltech.edu
    Competing interests
    No competing interests declared.
  4. Markus Meister

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    For correspondence
    meister@caltech.edu
    Competing interests
    Markus Meister, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2136-6506

Funding

Simons Foundation (543015)

  • Markus Meister

Simons Foundation (543025)

  • Pietro Perona

National Science Foundation (1564330)

  • Pietro Perona

Google

  • Pietro Perona

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to animal protocol 1656 approved by the institutional animal care and use committee (IACUC) at Caltech.

Reviewing Editor

  1. Mackenzie W Mathis, EPFL, Switzerland

Publication history

  1. Received: December 31, 2020
  2. Accepted: June 30, 2021
  3. Accepted Manuscript published: July 1, 2021 (version 1)
  4. Version of Record published: July 21, 2021 (version 2)

Copyright

© 2021, Rosenberg et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,324
    Page views
  • 601
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    András Ecker et al.
    Research Article

    Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ('replayed'), either in the same or reversed order, during bursts of activity (sharp wave-ripples; SWRs) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-driven model of area CA3 of the hippocampus. Our results show that the chain-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.

    1. Computational and Systems Biology
    2. Medicine
    James A Timmons et al.
    Short Report

    Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.