1. Neuroscience
Download icon

Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

Research Article
  • Cited 2
  • Views 1,547
  • Annotations
Cite this article as: eLife 2021;10:e56491 doi: 10.7554/eLife.56491

Abstract

Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how neural activity in the hippocampus is modulated during these two different behavioral processes. To explore this question, we performed calcium imaging from a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Our findings reveal that distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning, as learning emerges. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs and found that CA1 network connectivity patterns also differ between conditioning and extinction, even though the overall connectivity density remains constant. Together, our results demonstrate that distinct populations of hippocampal CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.

Data availability

All custom software will be made available on the Han Lab Github, and links are provided in the manuscript.All data generated during this study is included in the manuscript.

Article and author information

Author details

  1. Rebecca A Mount

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8962-1641
  2. Sudiksha Sridhar

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kyle R Hansen

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2782-7289
  4. Ali I Mohammed

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Moona E Abdulkerim

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Robb Kessel

    Biomedical Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Bobak Nazer

    Electrical and Computer Engineering Department, Boston University, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Howard J Gritton

    Biomedical Engineering Department, Boston University, Boston, United States
    For correspondence
    hgritton@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3194-3258
  9. Xue Han

    Department of Biomedical Engineering, Boston University, Boston, United States
    For correspondence
    xuehan@bu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3896-4609

Funding

National Science Foundation (CBET-1848029)

  • Xue Han

National Institutes of Health (1R01MH122971-01A1,1R21MH109941-01)

  • Xue Han

Boston University Dean's Catalyst Award

  • Xue Han

National Academy of Engineering

  • Xue Han

The Grainger Foundation, Inc.

  • Xue Han

National Science Foundation (DGE-1247312)

  • Kyle R Hansen

National Institutes of Health (F31 NS 105420)

  • Kyle R Hansen

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

Ethics

Animal experimentation: All animal procedures were approved by the Boston University Institutional Animal Care and Use Committee (protocol #201800680), and all experiments were performed in accordance with the relevant guidelines and regulations.

Reviewing Editor

  1. Joshua Johansen, RIKEN Center for Brain Science, Japan

Publication history

  1. Received: February 28, 2020
  2. Accepted: April 9, 2021
  3. Accepted Manuscript published: April 12, 2021 (version 1)
  4. Version of Record published: April 23, 2021 (version 2)

Copyright

© 2021, Mount 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

  • 1,547
    Page views
  • 225
    Downloads
  • 2
    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. Neuroscience
    Maude Bouchard et al.
    Research Article Updated

    Sleep slow waves are studied for their role in brain plasticity, homeostatic regulation, and their changes during aging. Here, we address the possibility that two types of slow waves co-exist in humans. Thirty young and 29 older adults underwent a night of polysomnographic recordings. Using the transition frequency, slow waves with a slow transition (slow switchers) and those with a fast transition (fast switchers) were discovered. Slow switchers had a high electroencephalography (EEG) connectivity along their depolarization transition while fast switchers had a lower connectivity dynamics and dissipated faster during the night. Aging was associated with lower temporal dissipation of sleep pressure in slow and fast switchers and lower EEG connectivity at the microscale of the oscillations, suggesting a decreased flexibility in the connectivity network of older individuals. Our findings show that two different types of slow waves with possible distinct underlying functions coexist in the slow wave spectrum.

    1. Neuroscience
    Michael A Barnett et al.
    Research Article Updated

    An important goal for vision science is to develop quantitative models of the representation of visual signals at post-receptoral sites. To this end, we develop the quadratic color model (QCM) and examine its ability to account for the BOLD fMRI response in human V1 to spatially uniform, temporal chromatic modulations that systematically vary in chromatic direction and contrast. We find that the QCM explains the same, cross-validated variance as a conventional general linear model, with far fewer free parameters. The QCM generalizes to allow prediction of V1 responses to a large range of modulations. We replicate the results for each subject and find good agreement across both replications and subjects. We find that within the LM cone contrast plane, V1 is most sensitive to L-M contrast modulations and least sensitive to L+M contrast modulations. Within V1, we observe little to no change in chromatic sensitivity as a function of eccentricity.