CV was calculated independently for each time bin and averaged across all stimuli. LFP variability was characterized from single-trial responses to each stimulus pattern. We calculated both the average SD throughout the response period and the mean correlation coefficient from all possible pairwise cross-correlation calculations. Linear discrimination with diagonal covariance BIBW2992 nmr matrix estimates was used for classification analyses. Separate data were used to train and test the classifier using the leave-one-out method (nine trials for training, one trial for testing, iterated ten

times per experiment). Separate classifiers were used for control and vM1 stimulation trials. For MUA classification, results using 20 ms binning are shown, although similar results were obtained for a range of spike histogram bin sizes. Frequency-dependent classification analyses used the time-domain filtered LFP signals, and we retrained the classifier for each frequency selleckchem band data set. Data are presented as mean ± SE, unless otherwise specified. Statistical testing was performed using Student’s t test, paired or unpaired as appropriate,

and one-way ANOVA or one-way repeated-measures ANOVA, for individual and population data, respectively. We thank Matthew Krause and James Mazer for many helpful discussions. We thank Flavio Frohlich for guidance on multielectrode recordings and analysis and Peter O’Brien for technical guidance. We thank Babak Tahvildari and Renata Batista-Brito for comments on the manuscript. This work was supported by NIH NS026143, NS007224 and Kavli Institute for Neuroscience (D.A.M.) and NIH F32NS077816 (E.Z.). “
“Combining inputs from different Resveratrol modalities improves stimulus detection, builds new representations and helps to resolve ambiguities (Stein and Stanford, 2008). Multisensory

integration (MI) occurs in the superior colliculus and in some cortical association areas, the degree of MI being dependent on timing, strength and spatial alignment of the stimuli (Stein and Wallace, 1996). A few studies examined the connectivity behind MI, e.g., in different cortical layers (Foxworthy et al., 2013). Cortical inputs are important for MI in the colliculus (Jiang et al., 2001), and GABAergic neurons are involved in cross-modal suppression in a cat multisensory area (Dehner et al., 2004). However, it is not clear how MI is organized within cortical microcircuits, and at the level of synaptic inputs and spike outputs. In primary sensory areas, stimulus representation is layer (de Kock et al., 2007, Martinez et al., 2005 and Sakata and Harris, 2009) and cell type specific, as shown by the different response properties of inhibitory and excitatory cells in primary visual cortex—V1 (Kameyama et al., 2010, Kerlin et al., 2010 and Runyan et al., 2010) and primary somatosensory cortex—S1 (Gentet et al., 2010 and Gentet et al., 2012).