### Procedures

Five Wistar adult rats (300 g – 350 g) were used in our experiments. They were anesthetized with urethane (130 mg/Kg) and the temperature of the animal was maintained at 37° by a servo-controlled heating pad.

Surgery consisted of exposing the infraorbital nerve as well as the two branches of the facial nerve (buccal and upper marginal mandibular) on the right side. The motor branches were dissected and transected proximally, and stimulation electrodes were placed on their distal stumps to produce the contraction of the mystacial muscles (Figure 1).

The deep vibrissal nerve innervating one vibrissal follicle (gamma) was identified with the high magnification of a dissecting microscope. We chose the gamma vibrissal nerve because of its easier surgical access. The gamma follicle is located on the last vertical row with three other whiskers (α, β, δ). The corresponding nerve is generally the most dorsally situated in the infraorbital nerve at the zygomatic arch region.

The dissected nerve was transected and a bipolar electrode was placed on it to record the afferent discharge of the corresponding vibrissa (Figure 1). The recording electrodes as well as the nerves were immersed in a mineral oil bath during all recording.

To obtain pressure 1, the tip of the whisker shaft was lightly placed on the surface (the platform surface was in a transverse position with respect to the whisker base). The remaining levels were obtained by moving the surface platform 3 mm closer for each following pressure. This procedure facilitates the whisker curving over the surface during the whisking [see Additional File 3].

All these procedures were done in accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals (National Research Council, NRC).

### Electrophysiological recordings

The experiments consisted in recordings of the multifiber activity of the gamma vibrissal nerve while the vibrissae were sweeping different surfaces. The signal recordings were obtained during active whisking (i.e., simultaneously with the electrical stimulation of the facial nerve and intrinsic muscle contraction).

Since our recordings are simultaneous with the vibrissae muscular activation, the stimulus artifact appears as the first signal followed by the deflection due to the muscle action current. Both deflections were removed before the data were processed and the start of the afferent discharge was estimated at 5 ms from the beginning of the recording. To differentiate the afferent discharge from the noise, we inactivated the follicular nerve by crushing at the end of the experiment. In this way we calculated the time end of the deflection due to the stimulus pulse and the extracellular muscular currents.

Three surfaces (wood, metal, acrylic) were polished using the same grade sandpaper P1000. This procedure allowed us to obtain surfaces with similar roughness and different textures.

The electrical stimulation was induced by a custom-made biological stimulator (developed at the Neuroscience Laboratory, Facultad de Medicina, Universidad Nacional de Tucumán, Argentina). Square-wave pulses (30 μs, 7V supramaximal, 5 Hz) simulated the vibrissal whisking at its natural frequency. An Isolation unit (ISA 100–234, Bioelectric Instruments) was used to isolate the animal from the stimulation device. The afferent nerve signals were filtered with a high-pass filter (fc = 150 Hz).

Each whisking was recorded in a window (sweep) of 100 ms. Fifty windows were obtained for each surface, and another fifty for each control (vibrissa sweeping the air). The controls were inserted between the surface recordings.

The infraorbital nerve signals were digitalized using a data acquisition system, Digidata 1322A, Axon Instruments, at 20 KHz. The parameters of the acquisition were controlled using the software AxoScope. The recordings were acquired immediately after a trigger sent from the electrical stimulator as is shown in a block diagram (Figure 1b).

### Digital processing and statistics

#### RMS value (Root Mean Square)

This parameter allows us to characterize the signal according to its energy content. The energy content was related to the amplitude of the signal in a certain interval of time. For a discrete signal, which consists in N samples equally spaced, the estimate is given by the following equation:

\begin{array}{cc}RMS=\sqrt{\frac{1}{N}{\displaystyle \sum _{k=1}^{N}{\left[x(k)\right]}^{2}}}& \text{k}=1,2,\dots ,\text{N}\end{array}\text{}\left(1\right)

Where, N, is the number of samples, x(k) is K-th sample of the signal, and RMS is the estimate of the energy.

#### Spectral Estimation – AR modeling

The AR model can be viewed as an all-pole, or infinite-impulse-response (IIR) filter whose current output, s_{n}, is a function of both the p most recent outputs, s_{n-1}, s_{n-2},..., s_{n-p}, and the current input, e_{pn}:

{s}_{n}={\tilde{s}}_{n}+{e}_{pn}=-{\displaystyle \sum _{i=1}^{p}{a}_{pi}\cdot {s}_{n-i}}+{e}_{pn}\text{}\left(2\right)

This AR filter can be specified in the frequency domain by taking the z-transform in equation (a). If E(z) and S(z) are the z-transforms of e_{p1}, e_{p2},...e_{pN} and s_{1}, s_{2},...s_{N} respectively, then:

*E*(*z*) = *A*(*z*)·*S*(*z*), where A(z)=1+{\displaystyle \sum _{i=1}^{p}{a}_{pi}\cdot {z}^{-i}}

{A}^{-1}(z)=\frac{S(z)}{E(z)}=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\left(1+{\displaystyle \sum _{i=1}^{p}{a}_{pi}\cdot {z}^{-i}}\right)$}\right.\text{}\left(3\right)

*A*^{-1}(*z*) is the AR model's transfer function, usually denoted by *H*(*z*). Its frequency response, *H*(*ω*), is determined by evaluating *H*(*z*) along the unit circle in the z-plane, where *z* = *e*^{jωT}for a sampling period, T. Furthermore, if *E*(*z*) is a white noise input sequence then its spectrum, *E*(*ω*), will be flat and the spectrum of the output sequence, *S*(*ω*) = *H*(*ω*)·*E*(*ω*), will be equal to *H*(*ω*) scaled by constant, *E*(*ω*) = *E*_{
p
}·T. In practice, however, *E*(*z*) only approximates a white noise sequence and so *S*(*ω*) can only be an estimate. This estimate \tilde{S}(*ω*), is given by:

\tilde{S}(\omega )=\raisebox{1ex}{${E}_{p}T$}\!\left/ \!\raisebox{-1ex}{${\left|1+{\displaystyle \sum _{i=1}^{p}{a}_{pi}\cdot {e}^{-ij\omega T}}\right|}^{2}$}\right.\text{}\left(4\right)

The assumption on which the AR modeling technique is based can now be rephrased in the frequency domain, where it is assumed that the flat spectrum of the white noise input sequence is "coloured" by the AR model to produce an output spectrum of the desire shape.

### Data processing

Only data between 5 ms and 100 ms were taken into account. This procedure does not include the discharges related to the muscular activation [24], and only the data obtained when the vibrissa was sweeping the air or surfaces were processed.

We consider all sweeps recorded for our analysis. The RMS (Root Mean Square) value was used as an estimator parameter of the signal energy [25]. The Power Spectrum Density (PSD) was calculated by using the Burg parametric estimation method [26]. Both methods were applied to all recordings, obtaining 50 RMS and 50 PSD values for each surface-pressure combination.

The PSD were represented using the maximum frequency (fmax). It was calculated within the range of 100 Hz to 600 Hz.

### Statistics

Statistical analysis was done with one way repeated measure ANOVA on ranks (Friedman) and Dunn's method as a post hoc test (software SigmaStat). RMS and fmax values were compared for recordings with the same level of pressure.

Data processing, RMS, PSD and fmax calculations were carried out by using MATLAB.

### Textures measurements

Surface texture is not a measurable quantity; it is not possible to assign a unique "texture" value to every different surface. However, it is possible to measure some of the intrinsic characteristics, or parameters, of surface texture. The International Standards BS.1134 and ISO 468 characterize the textures by means of "surface textures parameters". The surface textures parameters defined by the standard are the following:

1. Roughness, is a measure of the vertical characteristics of the surface.

2. Skewness, It is a non-dimensional parameter, which measures the symmetry of the surface about the mean plane.

3. Sharpness, the sharpness of the surface is defined by the kurtosis, another non-dimensional surface texture parameter.

4. Average wavelength (λ), is a measure of the spacing between peaks and valleys, taking into account their relative amplitudes and individual spatial frequency. The generalization of λ is difficult because its definition necessarily implies a direction.

We measured the materials roughness using a Hommel Tester T1000 (Hommel Werke) and used the *Ra* parameter (arithmetical deviation of the assessed profile) as a roughness estimation (International Standards BS.1134 and ISO 468) [see Additional File 2].