5°, thus ensuring that chattering and twitching were not included (Harvey et al., 2001), (3) the frequency of whisk cycles was between 4 and 20 Hz; and (4) in records based on EMG, the protractor and retractor muscles did not coactivate in phase, as occurs during twitching and chattering (Berg and Kleinfeld, 2003a). Voltage signals from the cortical and EMG microwire electrodes were impedance buffered, amplified, band-pass filtered from 1 Hz to 10 kHz and sampled at 36 kHz (Ganguly and Kleinfeld, 2004).

The cortical recordings were band-pass filtered between 600 Hz and 6 kHz (6 pole Butterworth filter run in forward and reverse directions) to isolate the spectral power of extracellular spike waveforms (Fee et al., 1996b). The voltage difference between the two EMG signals from each implanted muscle was calculated Paclitaxel concentration numerically, band-pass filtered between 400 Hz and 3 kHz (4 pole Butterworth filter run in forward and reverse directions), rectified, low-pass filtered at 250 Hz, and down-sampled to 1 kHz to form the differential rectified EMG signal (|∇EMG|). Cortical recordings were analyzed with an offline non-Gaussian cluster analysis algorithm to obtain check details single unit spike trains (Fee et al., 1996a). Putative

single units were accepted for analysis if the number of spikes that violated an imposed absolute refractory period of 2.5 ms was consistent with less than 10% level of contamination by unresolved units with Poisson spike rates. Further, the waveforms of the putative single units were visually inspected for separation from background noise and other waveform clusters obtained in the same recordings. We estimated

false-negative and false-positive errors (Hill et al., 2011) and found that 75% of our putative single unit clusters had a false-negative contamination of less than 10%, while 90% contained less than 20% contamination. In addition, that 88% of our putative single unit clusters had a false-positive contamination of less than 10%, while 95% contained less than 20% contamination. The relatively small false-positive before rate supports the claim that the same single units can code multiple stimulus dimensions (Figure 4, Figure 5 and Figure 7). These particular quality metrics could not be applied to the reevaluation of the data set from vS1 cortex (Figure S5). The correlation between a set of signals may be defined through the singular value decomposition (Golub and Kahan, 1965), a standard matrix factorization procedure that has previously been applied to determine correlations within space-time data (Prechtl et al., 1997). For the case of whisking motion across multiple vibrissae, we define the matrix Θ(x,t), where x labels the individual vibrissa and t is discrete time.