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.

Reviewing Editor

  1. Thomas Serre, Brown University, United States

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.

Version 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.

Metrics

  • 3,707
    views
  • 448
    downloads
  • 46
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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)

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

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

  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

Share this article

https://doi.org/10.7554/eLife.53445

Further reading

    1. Biochemistry and Chemical Biology
    2. Neuroscience
    Maximilian Nagel, Marco Niestroj ... Marc Spehr
    Research Article

    In most mammals, conspecific chemosensory communication relies on semiochemical release within complex bodily secretions and subsequent stimulus detection by the vomeronasal organ (VNO). Urine, a rich source of ethologically relevant chemosignals, conveys detailed information about sex, social hierarchy, health, and reproductive state, which becomes accessible to a conspecific via vomeronasal sampling. So far, however, numerous aspects of social chemosignaling along the vomeronasal pathway remain unclear. Moreover, since virtually all research on vomeronasal physiology is based on secretions derived from inbred laboratory mice, it remains uncertain whether such stimuli provide a true representation of potentially more relevant cues found in the wild. Here, we combine a robust low-noise VNO activity assay with comparative molecular profiling of sex- and strain-specific mouse urine samples from two inbred laboratory strains as well as from wild mice. With comprehensive molecular portraits of these secretions, VNO activity analysis now enables us to (i) assess whether and, if so, how much sex/strain-selective ‘raw’ chemical information in urine is accessible via vomeronasal sampling; (ii) identify which chemicals exhibit sufficient discriminatory power to signal an animal’s sex, strain, or both; (iii) determine the extent to which wild mouse secretions are unique; and (iv) analyze whether vomeronasal response profiles differ between strains. We report both sex- and, in particular, strain-selective VNO representations of chemical information. Within the urinary ‘secretome’, both volatile compounds and proteins exhibit sufficient discriminative power to provide sex- and strain-specific molecular fingerprints. While total protein amount is substantially enriched in male urine, females secrete a larger variety at overall comparatively low concentrations. Surprisingly, the molecular spectrum of wild mouse urine does not dramatically exceed that of inbred strains. Finally, vomeronasal response profiles differ between C57BL/6 and BALB/c animals, with particularly disparate representations of female semiochemicals.

    1. Neuroscience
    Kenta Abe, Yuki Kambe ... Tatsuo Sato
    Research Article

    Midbrain dopamine neurons impact neural processing in the prefrontal cortex (PFC) through mesocortical projections. However, the signals conveyed by dopamine projections to the PFC remain unclear, particularly at the single-axon level. Here, we investigated dopaminergic axonal activity in the medial PFC (mPFC) during reward and aversive processing. By optimizing microprism-mediated two-photon calcium imaging of dopamine axon terminals, we found diverse activity in dopamine axons responsive to both reward and aversive stimuli. Some axons exhibited a preference for reward, while others favored aversive stimuli, and there was a strong bias for the latter at the population level. Long-term longitudinal imaging revealed that the preference was maintained in reward- and aversive-preferring axons throughout classical conditioning in which rewarding and aversive stimuli were paired with preceding auditory cues. However, as mice learned to discriminate reward or aversive cues, a cue activity preference gradually developed only in aversive-preferring axons. We inferred the trial-by-trial cue discrimination based on machine learning using anticipatory licking or facial expressions, and found that successful discrimination was accompanied by sharper selectivity for the aversive cue in aversive-preferring axons. Our findings indicate that a group of mesocortical dopamine axons encodes aversive-related signals, which are modulated by both classical conditioning across days and trial-by-trial discrimination within a day.