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Author Notes:

Garrett B. Stanley: garrett.stanley@bme.gatech.edu

The authors have declared that no competing interests exist.

Subjects:

Research Funding:

This work was supported by the National Institutes of Health (NIH R01NS48285, U01NS094302 to GBS); Swiss National Science Foundation Early and Advanced Postdoc Mobility fellowship (AP); NSF CAREER Award (BCS-1753218 to BJH); Klingenstein-Simons Neuroscience Fellowship to BJH; and a Burroughs-Wellcome Collaborative Research Travel Grant to AJS.

State-aware detection of sensory stimuli in the cortex of the awake mouse.

Journal Title:

PLoS Computational Biology

Volume:

Volume 15, Number 5

Publisher:

, Pages e1006716-e1006716

Type of Work:

Article | Final Publisher PDF

Abstract:

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.

Copyright information:

© 2019 Sederberg et al

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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