When a significant main effect was detected with ANOVA tests, Bonferroni’s post hoc correction was used to determine significance

between pairwise comparisons. Normalized values are plotted as a percentage of the average value during the baseline period. Unless stated otherwise, reported values are mean ± SEM. For all statistical comparisons, asterisks indicate a significant effect at the following levels of significance: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. To assess INCB018424 the distribution of all pyramidal neurons in multidimensional space, we performed a K-means cluster analysis in MATLAB (MathWorks). First, we performed Student’s t tests on each electrophysiological property and morphological parameter to compare bursting and regular-spiking neurons. Using only those parameters that were significantly different, we constructed a 15-dimension matrix for all 110 neurons (consisting of seven morphological properties: total basal dendritic length, total tuft dendritic length, average basal branching

order, average tuft branching order, distance to main apical bifurcation, and the number of branch points in the basal Selleck ABT263 and tuft regions; as well as eight electrophysiological properties: input resistance, sag ratio, subthreshold dV/dt, ADP amplitude, threshold of the second spike, maximal dV/dt during the rising and falling phases of the second spike, and the FWHM of the first spike). Initial spike frequency Linifanib (ABT-869) was not included in the cluster analysis, though these values were significantly different between firing types. Based on these values, the K-means test selected k random cells to seed k clusters (n = 2–10). For all 15 normalized parameters, the Euclidian distance from these k seeds was calculated for all remaining cells, and each cell was then assigned to the cluster it was closest to. The cluster centers were then recalculated, and the process was repeated iteratively until the distributions ceased to change. To determine whether the computed clusters represent a single population or arise from multiple cell types, we computed a cluster index from the 15-dimensional matrix, defined as the ratio

of the sum of the square distances from each multidimensional point to its cluster center and the sum of the square distances from each point to the overall mean. This index varies from zero to one, with values close to zero corresponding to very tight clusters. Assuming that the cells were defined by a single multivariate Gaussian (the null hypothesis, which we would expect if these neurons belonged to the same cell type), we calculated a million cluster index values by repeatedly drawing 110 random samples from that distribution. The p value represents the likelihood that the simulated data have a cluster index greater than the experimental data. To determine whether k clusters (2–10) were represented in the data, we applied the jump method of Sugar and James (2003).