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


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.

SUBMITTER: Sederberg AJ 

PROVIDER: S-EPMC6561583 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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

Sederberg Audrey J AJ   Pala Aurélie A   Zheng He J V HJV   He Biyu J BJ   Stanley Garrett B GB  

PLoS computational biology 20190531 5


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  ...[more]

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