Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models

  1. Menoua Keshishian
  2. Hassan Akbari
  3. Bahar Khalighinejad
  4. Jose L Herrero
  5. Ashesh D Mehta
  6. Nima Mesgarani  Is a corresponding author
  1. Columbia University, United States
  2. Feinstein Institute for Medical Research, United States
  3. Hofstra Northwell School of Medicine, United States

Abstract

Our understanding of nonlinear stimulus transformations by neural circuits is hindered by the lack of comprehensive yet interpretable computational modeling frameworks. Here, we propose a data-driven approach based on deep neural networks to directly model arbitrarily nonlinear stimulus-response mappings. Reformulating the exact function of a trained neural network as a collection of stimulus-dependent linear functions enables a locally linear receptive field interpretation of the neural network. Predicting the neural responses recorded invasively from the auditory cortex of neurosurgical patients as they listened to speech, this approach significantly improves the prediction accuracy of auditory cortical responses, particularly in nonprimary areas. Moreover, interpreting the functions learned by neural networks uncovered three distinct types of nonlinear transformations of speech that varied considerably from primary to nonprimary auditory regions. The ability of this framework to capture arbitrary stimulus-response mappings while maintaining model interpretability leads to a better understanding of cortical processing of sensory signals.

Data availability

Source data files have been provided for Figures 1-3. Raw data cannot be shared as we do not have ethical approval to share this data. To request access to the data, please contact the corresponding author.

Article and author information

Author details

  1. Menoua Keshishian

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0368-288X
  2. Hassan Akbari

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Bahar Khalighinejad

    Department of Electrical Engineering, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jose L Herrero

    Feinstein Institute for Medical Research, Manhasset, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Ashesh D Mehta

    Deptartment of Neurosurgery, Hofstra Northwell School of Medicine, Manhasset, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7293-1101
  6. Nima Mesgarani

    Department of Electrical Engineering, Columbia University, New York, United States
    For correspondence
    nima@ee.columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2987-759X

Funding

National Institutes of Health (NIDCD-DC014279)

  • Menoua Keshishian
  • Hassan Akbari
  • Bahar Khalighinejad

National Institute of Mental Health

  • Jose L Herrero
  • Ashesh D Mehta

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

Ethics

Human subjects: All research protocols were approved and monitored by the institutional review board at the Feinstein Institute for Medical Research (IRB-AAAD5482), and informed written consent to participate in research studies was obtained from each patient before electrode implantation.

Reviewing Editor

  1. Thomas Serre, Brown University, United States

Publication history

  1. Received: November 8, 2019
  2. Accepted: June 21, 2020
  3. Accepted Manuscript published: June 26, 2020 (version 1)
  4. Version of Record published: July 9, 2020 (version 2)

Copyright

© 2020, Keshishian 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. Menoua Keshishian
  2. Hassan Akbari
  3. Bahar Khalighinejad
  4. Jose L Herrero
  5. Ashesh D Mehta
  6. Nima Mesgarani
(2020)
Estimating and interpreting nonlinear receptive field of sensory neural responses with deep neural network models
eLife 9:e53445.
https://doi.org/10.7554/eLife.53445
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Further reading

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    Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages.

    Funding: Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luftund Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), Foundation for OCD Research (FFOR).