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.
Highlights
The large majority of what we know about sensory cortex has been learned by averaging the response of individual neurons or groups of neurons across repeated presentations of sensory stimuli
Sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability
Spontaneous and sensory-evoked local field potentials (LFPs) were recorded using a 32-channel laminar array targeted to the region of the primary somatosensory cortex corresponding to facial vibrissae (S1 barrel cortex, Fig 1A)
Summary
The large majority of what we know about sensory cortex has been learned by averaging the response of individual neurons or groups of neurons across repeated presentations of sensory stimuli. Multiple studies in the last three decades have clearly demonstrated that sensory-evoked activity in primary cortical areas varies across repeated presentations of a stimulus, when the sensory stimulus is weak or near the threshold for sensory perception [1,2,3], and have suggested that this is an important aspect of sensory coding as the average response [4,5,6]. In an attempt to link the underlying neural variability to behavior, the principal framework for describing sensory perception of stimuli near the physical limits of detectability is signal detection theory [28]. A key prediction of signal detection theory is that, on single trials, detection of the stimulus is determined by whether the neural response to the stimulus crosses a threshold. From the perspective of an ideal observer, if variability in the sensory-evoked response can be forecasted using knowledge of cortical state, the observer could potentially make better inferences, but in traditional (state-blind) observer analysis, the readout of the ideal observer is not tied to the ongoing cortical state
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