Brains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. Here we developed 'Model identification of neural encoding (MINE)'. MINE is an accessible framework using convolutional neural networks (CNN) to discover and characterize a model that relates aspects of tasks to neural activity. Although flexible, CNNs are difficult to interpret. We use Taylor decomposition approaches to understand the discovered model and how it maps task features to activity. We apply MINE to a published cortical dataset as well as experiments designed to probe thermoregulatory circuits in zebrafish. MINE allowed us to characterize neurons according to their receptive field and computational complexity, features which anatomically segregate in the brain. We also identified a new class of neurons that integrate thermosensory and behavioral information which eluded us previously when using traditional clustering and regression-based approaches.

Data availability

All data generated in this study is publicly available. Links are provided in the 'Materials and Methods - Code and data availability' section.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Jamie D Costabile

    Department of Neuroscience, The Ohio State University, Columbus, United States
    Competing interests
    Jamie D Costabile, is employed by Hitachi Solutions America, Ltd., Irvine, CA.
  2. Kaarthik A Balakrishnan

    Department of Neuroscience, The Ohio State University, Columbus, United States
    Competing interests
    No competing interests declared.
  3. Sina Schwinn

    Department of Neuroscience, The Ohio State University, Columbus, United States
    Competing interests
    No competing interests declared.
  4. Martin Haesemeyer

    Department of Neuroscience, The Ohio State University, Columbus, United States
    For correspondence
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2704-3601


National Institutes of Health (5R01NS123887-02)

  • Jamie D Costabile
  • Kaarthik A Balakrishnan
  • Martin Haesemeyer

The Ohio State University Wexner Medical Center

  • Sina Schwinn
  • Martin Haesemeyer

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


Animal experimentation: Animal handling and experimental procedures were approved by the Ohio State University Institutional Animal Care and Use Committee (IACUC Protocol #: 2019A00000137 and 2019A00000137-R1).

Reviewing Editor

  1. Damon A Clark, Yale University, United States

Version history

  1. Preprint posted: September 3, 2022 (view preprint)
  2. Received: September 6, 2022
  3. Accepted: June 5, 2023
  4. Accepted Manuscript published: June 6, 2023 (version 1)
  5. Version of Record published: June 29, 2023 (version 2)


© 2023, Costabile 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.


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  1. Jamie D Costabile
  2. Kaarthik A Balakrishnan
  3. Sina Schwinn
  4. Martin Haesemeyer
Model discovery to link neural activity to behavioral tasks
eLife 12:e83289.

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