To determine whether the spatial tuning curve of a single neuron changed as time progressed on the treadmill, we used a two-factor ANOVA with spatial bin

and “temporal” bin as two factors (MacDonald et al., 2011). We included only those spatial bins that were occupied at least once in each “time” bin (bins located within AAT) in the ANOVA. We considered a neuron as having a significant change in firing rate as a function of time when the ANOVA produced a main effect of time (p ≤ 0.05). selleck products To test the theory that the observed temporally-modulated firing patterns could be entirely explained by the movement of the rat through space (i.e., place fields), we used the spatial tuning curve for each individual neuron to predict the firing rate of that neuron at each point in time. We started by using the rat’s actual spatial position (x and y room coordinates) and spike counts (sampled at 30 Hz) to generate a traditional occupancy normalized spatial tuning curve based on the firing of each neuron as described above (using 1 camera pixel square bins [approximately 0.2 cm × 0.2 cm] and a standard deviation

of 3 pixels). Then we used the spatial tuning curve as a look-up table: for each video frame we looked up the rat’s actual spatial coordinates in the spatial tuning curve to predict the firing rate of the neuron in that video frame. The result is two vectors for each neuron: one containing the actual Volasertib spike counts for each video frame and another

containing the predicted firing rate based purely on the spatial tuning curve and the rat’s trajectory. We then divided the time spent on the treadmill into 200 ms bins and generated two occupancy-normalized temporal tuning curves for each neuron: (1) an empirical temporal tuning curve which gave the actual average firing rate of the neuron for each time bin and (2) a model temporal tuning curve which used the predicted firing rates to calculate the average firing rate for each time bin. We then (-)-p-Bromotetramisole Oxalate used a bootstrap method to generate confidence intervals around each temporal tuning curve. We generated N (N = 1,000) bootstrap samples by randomly sampling (with replacement) a subset of all the treadmill runs. For each bootstrap sample, we calculated a temporal tuning curve for both the actual (empirical) firing rates and predicted (model) firing rates, and then calculated the difference between these two tuning curves for each time bin. The result was N empirical tuning curves, N model tuning curves, and N difference curves which were used to generate 95% confidence bounds on each temporal tuning curve and the difference curve ( Figure 6). We considered significant any time bins in which zero fell outside the confidence bounds of the difference curve, and we considered the empirical and model curves different if they were significantly different in at least one time bin.