Two Unconventional Considerations On Oxacillin

We noted in each frame whether either of the forelimbs were in swing or stance phase. The swing phase was defined LY2109761 as starting when the paw was lifted off the ball, and ending when contact between the paw and the ball was reinitiated. For either forelimb, we calculated the step-evoked electrophysiological activity for each cell by triggering a 40-bin (or 1.32 s) episode of the activity on the start of the stance phase, and averaged these episodes for each cell, giving an average step-evoked response vector ��1,2,...,40. We computed a modulation index m for each cell by taking this vector and computing: m=maxi=140(��i)?mini=140(��i)maxi=140(��i)+mini=140(��i). To test for significance, we generated bootstrap samples by selecting for each step a random window from the corresponding binned electrophysiological trace. For the individual cells, we considered a modulation index to be significant if its associated z-score corresponded to a p-value Oxacillin �� 1.0836, p = 0.9243 for left limb, 0.1723 �� 2.0968, p = 0.9312 for right limb. Spillover: ?0.1011 �� 0.3651, p = 0.7764 for left limb, 0.0192 �� 0.4422, p = 0.9645 for right limb). This is not surprising, since it reflects the fact that that only a subset of the granule cells are well-modulated by the step cycle, which is to be expected given the variety of inputs to the granule cell layer. For the polar plot in Figure 4��figure supplement 2, we plotted the modulation to step cycle by taking the absolute deviation from the mean activity as the radius of the plot. That is, R(��)=|activity(��)?��i=0Nactivity(i��360N)N|, EPZ5676 cost where activity is the step-triggered activity vector as in Figure 4A mapped on 360�� (i.e., this works out to each degree in the plot corresponding to 5 ms). For ease of visualization, we subtracted from R the minimum value across phases and then divided by the maximum value, so R is between 0 and 1. We picked the phase of maximal modulation as the phase that maximized R. In Figure 4��figure supplement 2, we plotted the modulations as z scores by subtracting the mean of the step triggered activity vector for each recording and dividing by its standard deviation. HMM A two state Markov model was constructed using the Bayes Net Toolbox (https://code.google.com/p/bnt/). The output distribution was modelled as a mixture of four Gaussians. The observed data at time i consisted of a 360 ms window of the electrophysiological data starting at time i.