Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons
- University of British Columbia, Canada
- Capital Medical University, China
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
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
- 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
- Received: July 24, 2016
- Accepted: February 2, 2017
- Accepted Manuscript published: February 4, 2017 (version 1)
- 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.
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Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons
eLife 6:e19976.
https://doi.org/10.7554/eLife.19976
Further reading
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- Neuroscience
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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- Neuroscience
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
