This function shows a peak near the whisking frequency but is low

This function shows a peak near the whisking frequency but is low outside of this range (Figures 2B and 2D). Across all units, the value of SNR(f) was especially small for f ∼1 Hz (Figure 2E).

Thus, individual units selleck products are not reliable linear coders of whisking behavior on slow timescales. We conjecture that the coding of vibrissa motion involves both slow and fast control signals. To test this hypothesis, we first decompose the motion into slow and fast components. A Hilbert transform is used to extract a rapidly varying phase signal, ϕ(t), that increases from -π to π radians on each whisk cycle regardless of slow variations in amplitude and midpoint (Figure 3A); the interval (-π, 0) corresponds to protraction and (0, π) corresponds to retraction. Continuous

estimates of the amplitude, θamp(t), and midpoint, θmid(t), were calculated on each whisk cycle at ϕ(t) = 0 and ϕ(t) = ±π and interpolated for other time points (Figure 3B). As a consistency check on this parameterization, we reconstructed the position, θˆ(t), according to equation(1) θˆ(t)=θampcos[ϕ(t)]+θmid(t). The reconstruction of the vibrissa trajectory yields an absolute error of 2.7° between θ(t) and θˆ(t) as an average across time and behavioral sessions (Figure 3A). The high quality of the fit shows that the motion may be well represented in terms of a slowly varying amplitude and midpoint and a find more rapidly changing phase. This decomposition of the whisking motion allows us to construct the marginal probability density functions for the slow whisking parameters, denoted p(θamp), p(θmid), as well as for the fast parameter, p(ϕ). This is illustrated for all whisking bouts associated with the behavioral session from which the data in the example of Figure 3A was obtained (Figure 3C), along with the associated

cumulative distributions (Figure 3D). The nonuniformity in phase is consistent with faster retraction than protraction in the whisk cycle (Gao et al., 2001). Note that the probability densities p(θamp) and p(θmid) can vary between behavioral sessions and depend largely on the row and Tolmetin arc of the monitored vibrissa (Curtis and Kleinfeld, 2009). As a check on the stationarity of the slow variations across animals and trials, we computed the autocorrelations for both θamp and θmid across all animals and trials (Figure 3E). Both correlations decay slowly. The midpoint is correlated for well beyond 2 s, while the amplitude decays with a time constant of approximately 1 s. How well do the spike trains of single units report changes in the slow whisking parameters, θamp and θmid, as opposed to fast changes in phase, ϕ? As illustrated for three example units in Figure 4, we observe significant modulation of the spike rate for all three parameters.

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