1. Neuroscience
Download icon

Transformation of a temporal speech cue to a spatial neural code in human auditory cortex

  1. Neal P Fox
  2. Matthew Leonard
  3. Matthias J Sjerps
  4. Edward F Chang  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Netherlands
Research Article
  • Cited 0
  • Views 885
  • Annotations
Cite this article as: eLife 2020;9:e53051 doi: 10.7554/eLife.53051

Abstract

In speech, listeners extract continuously-varying spectrotemporal cues from the acoustic signal to perceive discrete phonetic categories. Spectral cues are spatially encoded in the amplitude of responses in phonetically-tuned neural populations in auditory cortex. It remains unknown whether similar neurophysiological mechanisms encode temporal cues like voice-onset time (VOT), which distinguishes sounds like /b/-/p/. We used direct brain recordings in humans to investigate the neural encoding of temporal speech cues with a VOT continuum from /ba/ to /pa/. We found that distinct neural populations respond preferentially to VOTs from one phonetic category, and are also sensitive to sub-phonetic VOT differences within a population's preferred category. In a simple neural network model, simulated populations tuned to detect either temporal gaps or coincidences between spectral cues captured encoding patterns observed in real neural data. These results demonstrate that a spatial/amplitude neural code underlies the cortical representation of both spectral and temporal speech cues.

Article and author information

Author details

  1. Neal P Fox

    Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0298-3664
  2. Matthew Leonard

    Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Matthias J Sjerps

    Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  4. Edward F Chang

    Department of Neurological Surgery, University of California, San Francisco, San Francisco, United States
    For correspondence
    edward.chang@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2480-4700

Funding

National Institutes of Health (R01-DC012379)

  • Edward F Chang

National Institutes of Health (F32-DC015966)

  • Neal P Fox

European Commission (FP7-623072)

  • Matthias J Sjerps

New York Stem Cell Foundation

  • Edward F Chang

William K. Bowes, Jr. Foundation

  • Edward F Chang

Howard Hughes Medical Institute

  • Edward F Chang

Shurl and Kay Curci Foundation

  • Edward F Chang

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 participants gave their written informed consent before surgery and affirmed it at the start of each recording session. The study protocol was approved by the University of California, San Francisco Committee on Human Research. (Protocol number 10-03842: Task-evoked changes in the electrocorticogram in epilepsy patients undergoing invasive electrocorticography and cortical mapping for the surgical treatment of intractable seizures)

Reviewing Editor

  1. Jonathan Erik Peelle, Washington University in St. Louis, United States

Publication history

  1. Received: October 25, 2019
  2. Accepted: August 21, 2020
  3. Accepted Manuscript published: August 25, 2020 (version 1)
  4. Version of Record published: September 10, 2020 (version 2)

Copyright

© 2020, Fox 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

  • 885
    Page views
  • 172
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, 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. Genetics and Genomics
    2. Neuroscience
    Marina Kovalenko et al.
    Research Article Updated

    Somatic expansion of the Huntington’s disease (HD) CAG repeat drives the rate of a pathogenic process ultimately resulting in neuronal cell death. Although mechanisms of toxicity are poorly delineated, transcriptional dysregulation is a likely contributor. To identify modifiers that act at the level of CAG expansion and/or downstream pathogenic processes, we tested the impact of genetic knockout, in HttQ111 mice, of Hdac2 or Hdac3 in medium-spiny striatal neurons that exhibit extensive CAG expansion and exquisite disease vulnerability. Both knockouts moderately attenuated CAG expansion, with Hdac2 knockout decreasing nuclear huntingtin pathology. Hdac2 knockout resulted in a substantial transcriptional response that included modification of transcriptional dysregulation elicited by the HttQ111 allele, likely via mechanisms unrelated to instability suppression. Our results identify novel modifiers of different aspects of HD pathogenesis in medium-spiny neurons and highlight a complex relationship between the expanded Htt allele and Hdac2 with implications for targeting transcriptional dysregulation in HD.

    1. Computational and Systems Biology
    2. Neuroscience
    Pedro J Gonçalves et al.
    Research Article Updated

    Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.