Figure 1. Self-Motion tuning in parietal cortex is invariant across cortical laminae. See also Figs. S1-5, and Tables S1-2.

A. Left Two Plots. Multi-unit activity (MUA) recorded for a single day’s recording session and from a single tetrode were classified as having a preferred-self motion state if the self-motion maps for two behavioral sessions (from the whole day recording session) were significantly positively correlated. Self-motion maps from two behavioral sessions and corresponding correlation value are shown for one MUA example. Right. The shuffled distribution and critical r-value corresponding to the 99^th percentile. B. Left. The random shuffle distribution for the within-session correlation values from A (black bars) but with a different bin-size to better match the frequency counts across histograms shown here. The full distribution of random shuffle critical values (red bars with 70% opacity) and the full distribution of significant within-session correlation values from all data analyzed for the present paper (blue bars). Right. Example of a self-motion map with lower (r=0.31) within-session stability. Example of a self-motion map with higher within-session stability is shown in A. C. Same as in A; however, data came from two separate recording sessions obtained when the tetrode was at two depths (700µm above and 1400µm below). Black outline on lower motion rate map illustrates that for cross-depth comparisons behavior can vary considerably, and this analysis is limited to common data points. D. The sorted correlation value for MUA for each pair of depths where the tetrode was moved at least 100µm and the session data for each depth met the significance criteria described in A. Pairs of depths with significantly correlated motion maps were colored red (calculated as described in A, but across depth as in C). Thus, self-motion tuning is consistent across cortical depths for a particular tetrode. Data comes from all tetrodes that met this criteria from all rats.