Fig. 3: Illustration of neural analyses.

See also Supplementary Fig. 2. a, Schematic of the task structure, with the time windows used for analysis in grey. In b–d, left (blue) and right (red) stimuli are used as example task variables, and the neural traces are coloured accordingly. b, Schematic of the decoding model, which quantifies neural population correlates with task variables. Regularized logistic or linear regression is used to map spike counts in the relevant time windows (grey zone) from cells in each area into predictions of the values of the variables. c, Schematic of the single-cell analysis, which quantifies single-cell neural correlates with task variables. A conditioned combined Mann-Whitney U statistic is used to compute how sensitive the activities (in the grey zone) of single cells are to individual task variables, controlling for the values of other variables. d, Schematic of the population trajectory analysis, which describes the time evolution of the across-session neural population response, pooling cells across all recordings per region. The mean activities of every cell across their entire session for the different values of task variables are segmented into short bins, and used to define trajectories in a high-dimensional state space (projected, purely for visualization, into 3D). The distances between the trajectories for the different values of the task variables (arrows) define the separation. e, Schematic of the encoding model, which uses multiple linear regression of task-defined and behaviourally defined temporal kernels (the multicoloured traces) to fit the activity of single neurons.