The spike counts and occupancy times in each bin were independent

The spike counts and occupancy times in each bin were independently smoothed DAPT datasheet by convolving with a Gaussian smoothing kernel, then the spike counts were divided by the occupancy times to calculate the average firing rate. For spatial tuning curves (also referred to as spatial firing rate maps) in Figures 5 and S1, we used 1 cm × 1 cm bins and a circularly symmetrical Gaussian kernel with a standard deviation of 3 cm. For spatial tuning curves in Figure 6 and corresponding analysis we used 1 camera pixel square bins (approximately

0.2 cm × 0.2 cm) with a standard deviation of 3 pixels. For spatial tuning curves in Figure S3 we used 2 cm × 2 cm bins with a standard deviation of 6 cm. For temporal tuning curves (time spent on the treadmill, Figures 2, 3, 6, 7, and S2), we used 200 ms bins and a Gaussian kernel with a standard deviation of 600 ms. For distance (traveled on the treadmill) tuning curves (Figures 3, 7, and S2), we used 5 cm bins and a Gaussian kernel with a standard deviation of 15 cm. In the ensemble temporal tuning curves presented in Figure 3, each row represents the temporal tuning curve for a single neuron, normalized by dividing

by the peak firing rate of that neuron. For distance-fixed sessions, activity was plotted in units of distance, and for time-fixed sessions activity was plotted in units of time. All neurons active on the treadmill during a single session were included, sorted TGF-beta inhibitor by their peaking firing time or distance. To quantify a rat’s movement through physical space during treadmill running, we divided the space occupied during treadmill running into 1 cm × 1 cm bins and counted the number of video frames the rat Idoxuridine spent in each spatial bin. We then ranked the bins in order of decreasing time and counted the number of bins required to reach 75% of the total time spent on the treadmill. This number was then multiplied by the

area of each bin (1 cm2) to get the area that accounted for 75% of the time spent on the treadmill. We refer to this area as A75, and the smaller the value of A75, the less the rat moved through space while on the treadmill. We next quantified the degree to which the rat’s location systematically varied as a function of the time spent on the treadmill. To do this, we took either the distance (for distance-fixed sessions) or the time (for time-fixed sessions) spent on the treadmill and divided it into five evenly divided “time” bins. We then counted the number of spatial bins that were occupied at least once in each “time” bin and multiplied that number by 1 cm2 to get the area that was visited consistently across the entire treadmill run. We refer to this area as AAT (“AT” stands for “all time bins”) to distinguish it from A75. If the rat’s position systematically changed over the time spent on the treadmill, then AAT would be much smaller than A75.

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