For each K-means partition, the method computes

the distortion, i.e., a normalized squared distance between each observation and its closest cluster center. Since the K-means partitioning may depend upon the starting points used, the K-means algorithm is repeated a number of times with different starting conditions, and a mean distortion for each prescribed value of k is obtained. A distortion curve is then generated by plotting the mean distortion as a function of k. The distortion tends to decrease as the number of clusters is increased, and this is transformed into an increase by raising the distortion to CHIR-99021 cell line a negative power, Y. Because the distortion drops when the correct number of clusters is used, and remains roughly constant when even more clusters are employed, the transformed distortion exhibits a sudden increase, or jump, at the correct value of k. If one examines the size of the jumps in the transformed distortion, the largest jump is therefore an indication of the proper number of clusters. We also used MATLAB to perform a principal Alpelisib datasheet component analysis. We computed the principal components of the entire data set (30 properties) as well as the 15 properties that were significantly

different between the two populations. The authors would like to members of the Spruston laboratory for helpful discussions and Adam Hantman for reagents. A.R.G. was supported by F31 NS067758 and A.R.G. and S.J.M. were supported by T32 MH067564. The work was also supported by NIH RO1 NS35180, NS-046064, NS-077601, and the Howard Hughes Medical Institute. A.R.G., S.J.M., and N.S. designed the experiments. A.R.G. and S.J.M. collected the electrophysiological data, A.R.G. collected the morphological data, and

A.R.G. and E.B.B. performed the immunostaining and imaging. A.R.G. analyzed the data, with input from N.S. and help from W.L.K. to perform the cluster not and principal component analyses. A.R.G., B.B.M., and N.S. wrote the manuscript, with input from the other authors. “
“The NAc plays a major role in the generation of motivated behaviors (Berridge, 2007; Ikemoto, 2007; Nicola, 2007). It is thought to facilitate reward seeking by integrating dopaminergic reinforcement signals with glutamate-encoded environmental stimuli (Brown et al., 2011; Day et al., 2007; Flagel et al., 2011; Phillips et al., 2003; Stuber et al., 2008). A prominent idea is that the glutamate input to the NAc encodes the context, cues, and descriptive features that characterize any given moment in time (Berke and Hyman, 2000; Everitt and Wolf, 2002; Kelley, 2004; Pennartz et al., 2011). Together, glutamate and dopamine can promote synaptic plasticity, which is thought to be a crucial neural mechanism in the NAc by which pertinent environmental cues become more salient than other stimuli (Kheirbek et al., 2009; Sun et al., 2008; Wolf and Ferrario, 2010).