1. Neuroscience

Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons

  1. Dongsheng Xiao
  2. Matthieu P Vanni
  3. Catalin C Mitelut
  4. Allen W Chan
  5. Jeff M LeDue
  6. Yicheng Xie
  7. Andrew CN Chen
  8. Nicholas V Swindale
  9. Timothy H Murphy  Is a corresponding author
  1. University of British Columbia, Canada
  2. Capital Medical University, China
https://doi.org/10.7554/eLife.19976
Share this article
Cite this article
  1. Dongsheng Xiao
  2. Matthieu P Vanni
  3. Catalin C Mitelut
  4. Allen W Chan
  5. Jeff M LeDue
  6. Yicheng Xie
  7. Andrew CN Chen
  8. Nicholas V Swindale
  9. Timothy H Murphy
(2017)
Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons
eLife 6:e19976.
https://doi.org/10.7554/eLife.19976
  2. Download BibTeX
  3. Download .RIS

Abstract

Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.

Article and author information

Author details

  1. Dongsheng Xiao

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1669-0021

  2. Matthieu P Vanni

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  3. Catalin C Mitelut

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  4. Allen W Chan

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  5. Jeff M LeDue

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  6. Yicheng Xie

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  7. Andrew CN Chen

    Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  8. Nicholas V Swindale

    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.

  9. Timothy H Murphy

    Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    For correspondence
    thmurphy@mail.ubc.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0093-4490

Funding

Canadian Institutes of Health Research (MOP-12675)

  • Timothy H Murphy

Canadian Institutes of Health Research (FDN-143209)

  • Timothy H Murphy

International Alliance of Translational Neuroscience (N/A)

  • Dongsheng Xiao

Brain Canada, Canadian Neurophotonics Platform

  • Jeff M LeDue
  • Timothy H Murphy

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

Reviewing Editor

  1. David Kleinfeld, University of California, San Diego, United States

Ethics

Animal experimentation: Animal protocols (A13-0336 and A14-0266) were approved by the University of British Columbia Animal Care Committee and conformed to the Canadian Council on Animal Care and Use guidelines.

Version history

  1. Received: July 24, 2016
  2. Accepted: February 2, 2017
  3. Accepted Manuscript published: February 4, 2017 (version 1)
  4. Version of Record published: February 27, 2017 (version 2)

Copyright

© 2017, Xiao 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

  • 6,612
    views
  • 1,219
    downloads
  • 104
    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. Dongsheng Xiao
  2. Matthieu P Vanni
  3. Catalin C Mitelut
  4. Allen W Chan
  5. Jeff M LeDue
  6. Yicheng Xie
  7. Andrew CN Chen
  8. Nicholas V Swindale
  9. Timothy H Murphy
(2017)
Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons
eLife 6:e19976.
https://doi.org/10.7554/eLife.19976

Share this article

https://doi.org/10.7554/eLife.19976

Categories and tags

Research organism

Further reading

    1. Neuroscience

    Determinantal point process attention over grid cell code supports out of distribution generalization

    Shanka Subhra Mondal, Steven Frankland ... Jonathan D Cohen
    Research Article

    Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization – successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) – a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.

    1. Neuroscience

    Bidirectional fear modulation by discrete anterior insular circuits in male mice

    Sanggeon Park, Yeowool Huh ... Jeiwon Cho
    Research Article

    The brain’s ability to appraise threats and execute appropriate defensive responses is essential for survival in a dynamic environment. Humans studies have implicated the anterior insular cortex (aIC) in subjective fear regulation and its abnormal activity in fear/anxiety disorders. However, the complex aIC connectivity patterns involved in regulating fear remain under investigated. To address this, we recorded single units in the aIC of freely moving male mice that had previously undergone auditory fear conditioning, assessed the effect of optogenetically activating specific aIC output structures in fear, and examined the organization of aIC neurons projecting to the specific structures with retrograde tracing. Single-unit recordings revealed that a balanced number of aIC pyramidal neurons’ activity either positively or negatively correlated with a conditioned tone-induced freezing (fear) response. Optogenetic manipulations of aIC pyramidal neuronal activity during conditioned tone presentation altered the expression of conditioned freezing. Neural tracing showed that non-overlapping populations of aIC neurons project to the amygdala or the medial thalamus, and the pathway bidirectionally modulated conditioned fear. Specifically, optogenetic stimulation of the aIC-amygdala pathway increased conditioned freezing, while optogenetic stimulation of the aIC-medial thalamus pathway decreased it. Our findings suggest that the balance of freezing-excited and freezing-inhibited neuronal activity in the aIC and the distinct efferent circuits interact collectively to modulate fear behavior.

    1. Neuroscience

    Fundamental processes in sensorimotor learning: Reasoning, refinement, and retrieval

    Jonathan S Tsay, Hyosub E Kim ... Richard B Ivry
    Review Article

    Motor learning is often viewed as a unitary process that operates outside of conscious awareness. This perspective has led to the development of sophisticated models designed to elucidate the mechanisms of implicit sensorimotor learning. In this review, we argue for a broader perspective, emphasizing the contribution of explicit strategies to sensorimotor learning tasks. Furthermore, we propose a theoretical framework for motor learning that consists of three fundamental processes: reasoning, the process of understanding action–outcome relationships; refinement, the process of optimizing sensorimotor and cognitive parameters to achieve motor goals; and retrieval, the process of inferring the context and recalling a control policy. We anticipate that this ‘3R’ framework for understanding how complex movements are learned will open exciting avenues for future research at the intersection between cognition and action.