After data processing the 12 samples were grouped according to two parameters: genotype and drug

l position. Since we did not find any glaring differences in the ISI histograms over the whole recording session, we next asked how predictably place cells fired at each position pixel. We used spatial regression to estimate Torin 1 site firing rate at each position pixel. If spatial regression does a good job of predicting firing rate given the position at new places, one could say that the regression has captured the positiondependent firing rate characteristics of the place cell. A good regression fit would explain high amount of variance in firing rate across all position pixels. We saw that the percent of variance in pixel-by-pixel firing rate explained by regression on position was lower for T305D mice compared to WT mice. In other words, having access to the animal’s position was less useful in predicting place cell firing rate for T305D mutants. This result is not very surprising, since spatial coherence for T305D mice was also lower. Spatial coherence measures the correlation in firing rate between a position pixel and the neighboring 8 pixels around it, over the whole area. The spatial regression utilized here differs from spatial coherence, since the data fit utilizes firing information over a larger number of pixels in the neighborhood of each pixel to predict its firing rate. The neighborhood size could potentially include the whole area, if necessary. We also adapt the neighborhood size for each cell, and fit using a neighborhood size through a smoothing parameter that yields the best results, as described in the next parameter. We think the spatial regression utilized here is a better estimate of inter-dependence of local firing rate of place cells than spatial coherence, since it is not dependent on pixel sizes but on the smoothness of the firing rate place field. We now discuss the choice of spatial smoothing parameter. One of the PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/22188681 shortcomings of spatial coherence or spatial information measures is that they are closely dependent on the size of each position pixel. Larger pixels would smooth the place field more, and result in higher spatial coherence or lower spatial information. Too small a pixel size on the other hand would result in lower spatial coherence or higher spatial information. Rather than comparing firing rate correlation between a pixel and its surrounding or departure in the distribution from uniform firing rate, we asked what is the best prediction job one can do using available place dependent firing rate information irrespective of the extent of spatial neighborhood used for the prediction. With this thought, we think the simplest plausible method was to use a spatial regression with a smoothing parameter individually tuned to each place cell. To arrive at the best smoothing parameter, we used a 10-fold cross-validation over a wide range of smoothing parameters and picked the best parameter for each cell. Generally, we found that CA1 Place Cell Spiking in aCaMKIIT305D Mutant Mice then performing an average of these spline fits for each group to get the average for each group. Point-wise 25th and 75th percentile of these spline fits constitute the thin-lines. Thinner lines in Decreased reliability of bursts as position indicators. Since bursts are fewer in number compared to the lower fraction of total bursts was contained in the PF compared to WT. This leads us to conclude that bursts are more variable and thus are a less reliable signal of position in T305D mice. We also repeated the same analysis by redefining t