Extended Data Fig. 6: The discriminability of the two stimuli based on their evoked neural dynamics fluctuated trial-by-trial in a way that was highly correlated between cortical areas.

(a) Example scatter plot for an individual mouse in which the instantaneous stimulus decoder scores based on the activity patterns of cortical area PPC are plotted against those for cortical area RSC. Each data point shows results for an individual trial, at 0.5 s after stimulus onset, for Go trials (blue data points) or No-Go trials (black data points). Stimulus decoder scores for the two brain areas exhibit positively correlated trial-to-trial fluctuations. (b) Traces showing the mean time-dependent correlations of the fluctuations in instantaneous stimulus decoder scores for 8 different cortical areas and each of the other 7 brain areas within the imaging field-of-view. For most pairs of brain areas, these correlated noise fluctuations in decoder scores attained their maximum shortly after stimulus onset and then gradually decayed. Decoder training and testing was limited in this analysis to trials that the mice performed correctly. Shading: s.e.m. over N = 6 mice. Vertical dashed lines demarcate the stimulus presentation, delay and response intervals. (c) Two plots showing examples of stimulus-coding cells whose responses were modulated by the mouse’s response. Each plot shows the mean rate of Ca²⁺ events in an individual neuron, as a function of time relative to stimulus onset at t = 0, for the 4 different trial-types. The cell of the top plot is from area MV, and the cell of the bottom plot is from PPC. Both cells had P-values of <0.01 for stimulus-coding on Lick and No-Lick trials, and also had P < 0.01 for response-coding on GO-trials). We determined P-values through comparisons to trial-shuffled datasets (1000 different sub-samplings and random permutations of trials using equal numbers of trials of both stimulus- or response-types). The separation between the traces for Hit and Miss trials shows the extent of response-related modulation on trials with a Go stimulus. Shading: s.e.m. over trials (410 Hit trials, 218 Miss trials, 665 Correct Rejection trials, 100 False Alarm trials). (d) To determine if the elevated correlated noise fluctuations along the stimulus-coding direction within the interval [0.2 s, 0.5 s] after stimulus onset (when correlations were at their peak) reflects choice information relating to the formation of a motor response plan, we computed for each stimulus-type the proportion of the neural activity variance along the stimulus-coding direction that co-varied with the mouse’s upcoming motor response. The results show that only a tiny percentage (0.5% on average) of the variations in stimulus-coding can be explained as reflecting the mouse’s decision or response. Blue-shaded points denote data from individual mice. Red points are averages across mice. See also Fig. 5e. (e) Peak values of the time-dependent decoder score noise correlations (r), determined as in b, for all pairs of imaged brain areas for an example mouse, using either the data from each of five different imaging sessions, or the aggregated set of data from all imaging sessions. Fluctuations of decoder scores were correlated between sensory cortical areas during all recording sessions. The same general pattern of correlations between brain areas was visible in every session.