Response outcome gates the effect of spontaneous cortical state fluctuations on perceptual decisions

Sensory responses of cortical neurons are more discriminable when evoked on a baseline of desynchronized spontaneous activity, but cortical desynchronization has not generally been associated with more accurate perceptual decisions. Here we show that mice perform more accurate auditory judgements when activity in the auditory cortex is elevated and desynchronized before stimulus onset, but only if the previous trial was an error, and that this relationship is occluded if previous outcome is ignored. We confirmed that the outcome-dependent effect of brain state on performance is neither due to idiosyncratic associations between the slow components of either signal, nor to the existence of specific cortical states evident only after errors. Instead, errors appear to gate the effect of cortical state fluctuations on discrimination accuracy. Neither facial movements nor pupil size during the baseline were associated with accuracy, but they were predictive of measures of responsivity, such a..., The full dataset has been pre-processed as described in the section titled Data Set in the Materials and Methods section of the eLife paper (https://doi.org/10.7554/eLife.81774).
The data reported here is relative to 20 recordings and behavioral sessions from 5 mice.
Neuronal data, in particular the description on how we estimated the synchrony measurement is described in the section titled Estimation of baseline firing rate and synchrony in the Materials and Methods section of the eLife paper.
Neuronal data, as well as data extracted from videos has been 'whiten' as described in the Innovations section in the Materials and Methods section of the eLife paper.
Any further information can be found in the Materials and Methods section of the eLife paper., This dataset allows to reproduce the main results in Figures 3-5 and specifically all the analyses using generalized linear mixed models.
CSV files contain behavioral and neuronal data (overall rate and synchrony of neuronal populations in auditory cortex) and can be opened with any common software (Python, MATLAB/GNU Octave, R, etc).
CSV files contain the variables listed in the corresponding GLMMs.

File name
Figure in the paper
GLMM used

data_correct_aftervalid.csv
Figure 3A
Correct ∼ 1 + pCorr + Stim + TrN + OpticFI + PupilSI + FRI + SynchI + ( 1 + pCorr + Stim + TrN + OpticFI + PupilSI + FRI + SynchI | session)

data_correct_aftererror.csv
Figures 3C-D
Correct ∼ 1 + Stim + TrN + OpticFI + PupilSI + FRI + SynchI + ( 1 + Stim + TrN + OpticFI + PupilSI + FRI + SynchI | session)

data_correct_aftercorrect.csv
Figures 3E-F
Correct ∼ 1 + Stim + TrN + OpticFI + PupilSI + FRI + SynchI + ( 1 + Stim + TrN + OpticFI + PupilSI + FRI + SynchI | session)

data_outcomeprevious.csv
Fig...