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Department of Physiology, Zlotowski Center for Neuroscience, Ben-Gurion University, Israel
Submitted 25 February 2008; accepted in final form 14 September 2008
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ABSTRACT |
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INTRODUCTION |
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The system is highly specialized for processing fine tactile information acquired by the array of whiskers on the facial mystacial pad. Rats actively sweep their whiskers across surfaces in a rhythmic forward and backward motion with a frequency ranging from 5 to 25 Hz, called whisking, to locate and distinguish objects in the animal's immediate sensory environment (Berg and Kleinfeld 2003
; Bermejo et al. 2002; Carvel and Simons 1990; Sachdev et al. 2001). Whisker movements excite several hundred primary afferent fibers that innervate mechanoreceptors on each whisker shaft (Ebara et al. 2002). These signals travel along the sensory nerve to the brain stem. The axons of the brain stem neurons then cross the brain midline and travel to the thalamic somatosensory nuclei. Thalamic neurons project to the primary somatosensory cortex, conveying information to layer 4 cell populations called "barrels" (Welker 1971; Woolsey and Van der Loos 1970). Psychophysical studies have demonstrated that rats can reliably detect, using their whiskers, small differences in surfaces textures (Carvel and Simons 1990, 1995; Guic-Robles et al. 1989; Ritt et al. 2008; von Heimendahl et al. 2007). Thus the somatosensory system appears well suited for conveying and processing complex sensory information rapidly and reliably (Diamond et al. 2008).
What mechanisms might underlie this striking capacity for texture discrimination? One attractive model posits that the resonant properties of whiskers, which vary systematically with whisker length along whisker rows, will mediate texture discrimination (Brecht et al. 1997; Hartmann et al. 2003; Kleinfeld et al. 2006; Mehta and Kleinfeld 2004; Moore and Andermann 2005; Neimark et al. 2003). When rats actively sweep their whiskers across a surface, that vibrissa whose resonance frequency most closely matches the texture-induced input frequency will transmit the largest vibrations to the follicle (Andermann et al. 2004). Different whiskers would be tuned to different frequencies and thus split the tactile vibration signals into labeled frequency lines in the cortex (Andermann et al. 2004; Hartmann et al. 2003; Mehta and Kleinfeld 2004; Moore 2004; Moore and Andermann 2005; Neimark et al. 2003). An alternative view suggests that the mechanical properties of the vibrissae determine the translation of surface roughness into different intrinsic frequency or velocity modes in each vibrissa (Andermann et al. 2004; Hipp et al. 2006). The extent to which different modes are favored within each vibrissa reflects the differences between surfaces (Fend et al. 2003; Mehta and Kleinfeld 2004). The neural representation of these modes can either be expressed in discharge probabilities that increase in proportion to stimulus velocity (Arabzadeh et al. 2003–2006; Jones et al. 2004; Shoykhet et al. 2000) or by the preservation of the velocity profile of the vibration in the temporal pattern of spikes (Arabzadeh et al. 2005, 2006; Deschenes et al. 2003; Gibson and Welker 1983a,b; Hipp et al. 2006; Lichtenstein et al. 1990; Shoykhet et al. 2000).
Recent seminal studies, using artificial whisking across textures, have concluded that the velocity profile signal in whiskers transmits texture coarseness to the whisker shaft (Albarracín et al. 2006; Arbzadeh et al. 2003–2005; Kleinfeld et al. 2006). Texture discrimination can then be achieved using cortical spike rate and patterns (Arbzadeh et al. 2006). While these studies have advanced our understanding of the representation of "natural" stimuli such as textures in the rat's whisker somatosensory system, several key issues remain unresolved: what are the characteristics of texture-related whisker vibrations that are transmitted to the whisker shaft? How are these vibrations translated to trigeminal ganglion (TG) neuronal discharges? Are these features able to support texture discrimination? What are the functional consequences of large variability in this translation process? To address these issues, we induced artificial whisking across textures in anesthetized rats, monitored whiskers’ vibrations at the shaft, and concurrently recorded from TG neurons. We first examined how whiskers’ motion along a surface is converted to discharge patterns in TG neurons. Then we characterized the texture-related whisker vibrations that underlie these discharge patterns. Using linear discriminant analysis (LDA), we were able to determine whether whisker vibrations are able to support texture discrimination and the extent to which it depends on intrinsic and extrinsic variables.
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METHODS |
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Adult male Sprague Dawley rats (n = 50; 250–350 g) were used. All experiments were conducted in accordance with international and institutional standards for the care and use of animals in research. Surgical anesthesia was induced by urethan (1.5 g/kg ip) and maintained at a constant level by monitoring forepaw withdrawal and corneal reflex and administering extra doses (10% of original dose) as necessary. Atropine methyl nitrate (0.3 mg/kg im) was administered after general anesthesia to prevent respiratory complications. Body temperature was maintained near 37°C using a servo-controlled heating blanket (Harvard, Holliston, MA).
Recording and stimulation
After placing subjects in a stereotaxic apparatus (TSE, Bad Homburg, Germany), an opening was made in the skull overlying the trigeminal ganglion, and tungsten microelectrodes (2 M
, NanoBio Sensors) were lowered according to known stereotaxic coordinates of NV (1.5–3 ML, 0.5–2.5 AP) (Leiser and Moxon 2006; Shoykhet et al. 2000) until units drivable by whisker stimulations were encountered. The recorded signals were amplified (x1,000), band-pass filtered (1 Hz to 10 kHz), digitized (25 kHz), and stored for off-line spike sorting and analysis. The data were then separated to local field potentials (LFP; 1–150 Hz), and isolated single-unit activity (0.5–10 kHz). Spike extraction and sorting was accomplished with MClust (by A.D. Redish available from http://www.cbc.umn.edu/
redish/mclust), a Matlab (Mathworks, Natick, MA)-based spike-sorting software. The extracted and sorted spikes were stored at a 0.1-ms resolution and peristimulus time histograms (PSTHs) were computed.
Artificial whisking (Fig. 1A) (Brown and Waite 1974; Szwed et al. 2003) was induced by stimulating the buccolabialis motor branch of the facial nerve (Semba and Egger 1986). The nerve was cut and its distal end was mounted on bipolar tungsten electrodes and kept moist. Bipolar rectangle electrical pulses (10–15 pulses of 100 µs at 143 Hz for 70–105 ms) were applied through an isolated pulse stimulator (ISO-Flex; A.M.P.I.) to produce whisker protraction, followed by a passive whisker retraction at frequencies in the range of 6–12 Hz. The stimulation magnitude was adjusted at the beginning of each recording session to the minimum value that reliably generated the maximal possible movement amplitude (50–200 µA).
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Whisker movements were measured under different conditions: whisking with no object contact ("free whisk"), whisking on smooth surface (smooth), and whisking on sandpapers of five different grades [from fine-grained to coarse-grained (the numbers in the parentheses indicate the average grain diameter): P60 (269 µm), P100 (162 µm), P150 (100 µm), P220 (68 µm), and P320 (46 µm)]. These grades were chosen in accordance with previous studies (Arbazadeh et al. 2005; Hipp et al. 2006) and were also based on the findings that rats can discriminate a) between sandpapers with grain sizes of 400 and 2,000 µm (Guic-Robles et al. 1989) and b) between smooth surfaces and rough surfaces having grooves spaced at 90-µm intervals (Carvell and Simons 1990). The textures were mounted on a flat surface (15 x 15 mm). The surfaces were oriented so that the whisker rested on them and remained in contact during the entire whisk trajectory. The proximal edge of the surface was 
10 or 30 mm from the base of the whisker (see Methodological considerations for a detailed account of this stimulus configuration).
For each texture and full free whisking, we recorded 10 trials once every 60 s. Each trial contained 10 whisks at 6 or 12 Hz, giving a total of 100 whisks per stimulus. During each experiment, the textures were randomly interleaved. To monitor the drift in whisking and sensor performance, we measured whisker vibrations during free whisking between textures. To minimize the number of stimulus combinations and to cover all ranges of whisker types (Moore 2004; Moore and Andermann 2005; Neimark et al. 2003), we monitored the movements of the following whiskers: β, B2, A2, C3, B4 (Fig. 1B).
Data analysis
Whisker movements resulted in high-frequency micromotions (HFMs) superimposed on whisking macromotions (WMs; Fig. 1C). To separate HFM and WM signals, we used band-pass Butterworth type II filter of fourth order: cutoff frequencies 1–50 Hz and cutoff frequencies 50–300 Hz, respectively (Fig. 1, C and D, 1 and 2). Because responses of neurons during various stages of the whisker somatosensory system better reflect whisker deflection velocity rather than amplitude (Arabzadeh et al. 2004; Jones et al. 2004; Shoykhet et al. 2000), we also calculated the velocity profile of HFMs (Fig. 1D3). Whisker angle was computed at base (the most proximal point of the tracked whisker): angle at base = arctan (measured whisker trajectory/measuring distance), measuring distance 2 mm.
To determine whisk protraction and retraction onset and ending, we identified the trajectory inflection points at the beginning and ending of each phase (Fig. 1D1). Protraction onset and retraction ending were determined by locating the time points of the first and last peaks in the second derivative of whisker trajectory. Protraction ending and retraction onset were determined by locating the time points of the first and last troughs in the second derivative of whisker trajectory. We separated the protraction and retraction phases of each whisk (Fig. 1D1, gray background).
We selected a wide range of parameters that came from HFMs and WMs signals. WMs signals were characterized by: WMs amplitude, defined as the distance between the highest and the lowest points of whisker retraction; WMs velocity, defined as the 10–90% retraction slope. HFMs were calculated by initially converting the trajectory signal to velocity. An event was defined as the velocity trace comprising three successive local extrema (Fig. 1D, bottom). The scanning for micromotions started from retraction onset. The first extrema during the retraction period was set to be the starting point of the first event (peak –circle), the second – the peak (valley-diamond) of the first event, and the third – the end of the first event (peak –circle). The third point was also the first point of the second event, the fourth –the peak of the second event and so on. Shifting the frames for event detection did not alter the distribution of event parameters significantly (P << 0.001). To reduce the detection of spurious micromotions, we included an amplitude threshold criterion. The threshold was set as the mean micromotion amplitude ±3 SD. of a control segment (taken from the time between whisking bouts). HFMs with amplitudes lower than the threshold were not included in the analysis. An example of detected HFMs in the velocity signal (Fig. 1D3) is shown above the trace as a raster plot. Each HFM is indicated by a vertical line.
The following parameters characterizes HFMs: HFMs magnitude defined as the distance between the peak velocity and the average of the two valleys (Fig. 1D, bottom); HFMs latency is the value given to the average latency from retraction onset to all events within a whisks; Inter HFMs interval (ITI) defined as the time interval between two successive HFMs; number of HFMs defined as the number of HFMs occurring in one whisk. HFMs parameters from each whisk were represented by the average. The significance of the differences between the measured parameters was evaluated using one-way ANOVA. When significant differences were indicated in the F ratio test (P < 0.05), the Tukey method for multiple comparisons was employed to determine those pairs of measured parameters that differed significantly within the pair (P < 0.05 or P < 0.01). Averaged data are expressed as means ± SE. Error bars in all the figures indicate the SE.
We characterized the correlation between the spike train and the HFMs using the cross-correlogram (CCG) (Perkel et al. 1967), calculated using the methods of Bair et al. (2001). We represented the spike train of the cell and the HFMs that crossed threshold as binary time series’ with 1-ms resolution.
The CCG was then computed as follows
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is the time lag. A correction was applied to each CCG to compensate for the edge effects that arise when a correlation histogram is computed on windowed data because the spikes at the beginning and ending of the whisk time interval are surrounding by stimulus-elevated firing rates only on one side. The result is often a broad peak in the CCG. Each bin of the CCG was multiplied by the factor F
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Our objective in quantifying the synchrony between HFMs and spikes was to establish a measure of the magnitude and statistical significance of the temporal correlation between HFMs and spikes that occurred within ±10 ms of zero time lag. To determine the magnitude of correlation above and beyond that which might have been introduced by whisking, we used a modification of the method by Maldonado et al. (2000). We utilized the variance in the interval-shuffled control correlograms to establish the confidence limits for significance. Our measure, referred to as the significance ratio (SR), is the ratio of two integral values: a peak value (P; Fig. 3C, black + gray), representing the magnitude of HFMs spikes correlation, computed by taking the sum of the bins in the central 20 ms of the CCG, and a variance value (R; Fig. 3C, gray), representing the expected occurrence of coincident HFMs and spikes, computed from the sum of the central 20 ms in each histogram lying below the 95% confidence limits. This ratio measure is illustrated in Fig. 3 and was computed as follows
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The simulation was repeated 200 times for each whisk. The CCG was computed from each simulated spike train and corresponding correlograms were calculated. The resulting correlograms were edge corrected and the mean value for each bin (1 ms) was then computed. This yielded a set of 200 control values for each bin in experimental CCG. We assigned a confidence limit for statistical significance by choosing the R value for each bin in the control distributions that was >95% of all the values. To provide a measure of correlation strength that was independent of the total spike count in the data, we then calculated the SR value for each bin and averaged across all SR values in the central 20 ms of the CCG. Values of this SR >1.0 were considered significant.
Classification
Linear discriminant analysis (LDA) (Krzanowski 1988; Seber 1984) was used to evaluate whether vibration signals in whisker movements could predict surface roughness. Classification was accomplished using a set of variables that came from WMs and HFMs characteristics (see preceding text). This analysis was employed to statistically determine whether: first, whisker vibration signals can serve to discriminate between textures; second, the influence of whisker identity on texture discrimination; third, the influence of texture distance on texture discrimination; fourth, the influence of whisking frequency on texture discrimination. Here the multivariate data set consisted of WMs and HFMs parameters for each stimulus (see preceding text). For the different conditions, the 100 whisks in each session were divided randomly to "training" and "testing" whisks. For each texture in the training whisks, the analysis determines the location of the point that represents the means for all WMs and HFMs parameters in the multivariate space in the model. These points are called group centroids. An example of two-dimension multivariate space is shown in Supplementary Fig. S2A. For each of the testing whisks, the analysis computes the distances (of the respective whisk) from each of the training texture group centroids. This measure, which is normalized by the within-group variance (similar to z score), provides an indication of whether or not an observation is an outlier with respect to the independent variable values. Finally the analysis classifies the whisk as belonging to the group to which it is closest, that is, where the distance is smallest (Mahalanobian distance). This whisk-by-whisk prediction of texture identity was then compared with the real texture identity of each whisk, and a percentage of correctly predicted whisks was obtained for each group. This resulted in a hit matrix in which the diagonal reflects the percentage of correct predicted whisks (Supplementary Fig. S2B). The sum of all the values in the diagonal gives the overall correct classification. To correct for varying number of textures, we normalized the percentage of correct predicted whisks to the corresponding chance level. Pnormalized = 100*(Pcorrect – Pchance)/(100 – Pchance) where Pchance = 100/D. D = number of textures.
Linear discriminant analysis was accomplished with statistical toolbox in Matlab.
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RESULTS |
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An example of the experimental set is shown in Fig. 1A. Artificial whisking was induced by electrically stimulating cranial nerve 7, generating 6- and 12-Hz whisking movements (Brown and Waite 1974; Szwed et al. 2003) that resemble whisker trajectories in awake rats (Berg and Kleinfeld 2003). An optical sensor measured whisker displacements transmitted to the receptors in the follicle. Whisker movements were measured under different conditions: "free whisk," smooth surface, and sandpapers of five different grades [Fig. 1C; see METHODS; P60 (n = 14), P100 (n = 11), P150 (n = 23), P220 (n = 28), and P320 (n = 6)]. The textures were mounted on flat surface that was oriented so that the whiskers rested on it and remained in contact (see METHODS): β (n = 8), B2 (n = 8), A2 (n = 3), C3 (n = 3), B4 (n = 3) (Fig. 1B). Whisker movement across surfaces resulted in HFMs riding on WMs signals (Fig. 1C). To separate these, we employed band-pass filters (see METHODS) which resulted in the traces shown in Fig. 1D, 1 and 2.
Before going into a detailed description of our results, it is important to discuss some of our methodological considerations: first, because it was suggested that responses of neurons in various stages of the whisker somatosensory system better reflect whisker deflection velocity than amplitude (Arabzadeh et al. 2003–2006; Jones et al. 2004; Shoykhet et al. 2000), we used in the current study, the velocity profile of the HFMs (Fig. 1D3). A comparison between the discrimination abilities of trajectory and velocity signals reveals no significant differences between these two signals (supplementary material: part 2 and corresponding Fig. S3). Second, although whisking is not restricted to a single plane of motion (Bermejo et al. 2002), it was claimed elsewhere that movement in the anterior-posterior axis contains most texture-specific signals (Arbazadeh et al. 2005). To validate this assertion and to simplify our experimental paradigm, we have tested this claim in our experimental setup and found that the majority of texture-related tactile information is carried by the horizontal plane (see supplementary material: part 3 and corresponding Fig. S4 for a study of multi-dimension whisker signal). Therefore we examined the principal direction of vibrissae movements, corresponding to the horizontal forward-backward axis. Finally, during protraction, in some animals, we have seen movement modulation locked to the electrical stimulus (see also Szwed et al. 2003). Therefore we separated the protraction and retraction phases of each whisk (Fig. 1D1, gray background) and performed the complete analysis for each phase. Results were qualitatively similar for the two phases, although discrimination performance was slightly lower for the protraction phase. Therefore in the current study we restricted the analysis to the retraction phase (see supplementary material: part 4 and corresponding Fig. S5 for a comparison of texture-related signals during protraction and retraction phases).
To examine the relationship between whisker vibrations and neuronal discharges, we recorded extracellularly from 25 TG neurons. All these neurons could be driven by manual stimulation of one of the whiskers, and all had single whisker receptive fields. We first compared whisker vibrations and neuronal discharges during free whisking and during contact with textures. An example of the change in these variables between these two conditions is shown for a single neuron responding to B2 whisker in Fig. 2. The columns on the left and the right depict whisker vibrations and neuronal discharges during free whisking and texture contact, respectively. The top and bottom panels in A show all whisker trajectories and HFMs in gray, respectively. The average whisk and HFMs are marked in black. The panels in B show the raster plots of the threshold crossing events and spikes. We represented the spike train of the cell and the HFMs that crossed threshold as binary time series’ with 1 ms resolution. As can be seen in Fig. 2, B and C, during contact with textures, TG neurons increased their discharge rate in response to an increase in HFMs rate and amplitude (more examples are shown in Supplementary Fig. S6). These results suggest that while TG neurons may convey whisking information they increase their firing during contact with textures.
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To quantify the temporal relationship between neuronal discharges during HFMs, we calculated the SR ratio of the sum of bins in the central 20 ms of the experimental CCG that exceed the 99% confidence limits (black and gray area; see METHODS) and the sum of the central 20 ms in the corresponding CCG of the interval-shuffled events and spike trains lying below the 99% confidence limits (gray area). We found that this ratio ranges from 0.45 to 2.9 (Fig. 2E). Any experimental SR ratio value equal to or >1 was considered statistically significant. The degree of correlation as expressed in the SR value did not depend on the firing rate of the neurons (not shown) and was not related to whether the neurons fired during whisking in free air (Fig. 2). Together these results suggest that in addition to discharging throughout the whisk, most TG neurons responses are locked to HFMs.
We next sought to determine whether the characteristics of HFMs and WMs signals are able to support texture discrimination. We quantified the properties of the WMs and HFMs, by computing WMs amplitude, WMs velocity, HFMs magnitude, HFMs latency, ITI and number of HFMs (Fig. 1D; see METHODS). A distribution of these parameters is shown in Fig. 4 for a single B2 whisker presented with texture P220. For each parameter, we calculated the average within a whisk and plotted the distribution across all whisks (n = 100). Several points are noteworthy. First, HFMs parameters across whisks for a single texture are broadly distributed. This illustrates the highly dynamic nature of texture signals transmitted to the follicle. Second, whisking signals are highly variable, possibly reflecting an intrinsic variability in muscular activation.
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; Carvell and Simons 1990; Knutsen et al. 2005). Fifth, the range of HFMs magnitudes measured across several surfaces corresponds to the higher end of the range of micromotions used in passive stimulation (Arabzadeh et al. 2003; Hartings and Simons 1998; Pinto et al. 2000; Shoykhet et al. 2000; Temereanca and Simons 2003; Wilent and Contreras 2004, to name a few) In none of these studies were velocities >2.5°/ms employed. However, our results are well within the range of micromotion velocities reported in a recent study, in awake behaving rats (Fig. 8B in Ritt et al. 2008). Together these findings indicate that artificial whisking imitates some aspects of whisking in awake behaving rats and that the properties of whisker vibration signals are tightly linked to surface roughness.
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0.4. No significant differences were found between variables. These results indicate that whiskers vibrations provide a potential cue that might be used for the accurate discrimination of tactile stimuli.
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The analyses shown in Figs. 4–6 were done mostly on B2 whiskers. To determine the influence of the mechanical properties of the whiskers on the translation of surface roughness into whisker vibration signal, we compared the influence of different sandpapers (from fine-grained to coarse-grained) on the aforementioned characteristics in different whiskers. The results of these calculations are shown in Fig. 7A for β (n = 8) and C3 (n = 3) whiskers presented with different textures. This example shows that while in both whiskers, HFMs magnitude is dependent on surface roughness, their dependency profiles are different. The disparity between the different whiskers is further verified by the comparison between all studied variables in these whiskers in response to the same texture (Fig. 7B; P220). Because whiskers responses were taken from different animals, we normalized them to the highest value. The panel shows that in thin rostral whiskers, whisking velocity decreases while their amplitude increases. Examination of HFMs parameters reveals that their number and magnitude increases in rostral whisker. These results suggest that whiskers’ mechanical properties may influence the transformation of surface roughness into whisker vibrations.
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It has been suggested that texture or form discrimination may require the integration of signals from multiple vibrissae (Carvell and Simons 1995; Krupa et al. 2001; Neimark et al. 2003) to disambiguate differences in whisker properties and spatial frequency components among textures. Therefore we examined whether integration of tactile information from multiple whiskers would increase the discrimination between different textures. In this analysis, we used all variables from two whiskers resulting in 12 parameters for the LDA. Figure 7D shows the classification scores as expressed in percent correct for each whisker and for both. These calculations reveal that integration of information from multiple whiskers resulted in an enhancement of texture discrimination. Together these results suggest that the availability of tactile information from multiple channels may facilitate texture discrimination.
To determine the influence of surface location on texture discrimination, the proximal edge of the surface was placed at 10 and 30 mm from the base of the whisker. An example of whisker B2 responses to texture P100 at these two distances is shown in Fig. 8A. This example shows that surface distance has a critical influence on WMs and HFMs. The difference between responses at the different distances is further verified by a comparison between all studied variables in these B2 and β whiskers in response to the same texture (n = 4; P100) at two distances (Fig. 7B; because the influence of distance on whisker vibrations was similar in the 2 whiskers, the values in the plot are an average of the 2 whiskers normalized to the maximum response). The panel shows that at distant object location, WMs velocity and amplitude approach their values during free whisking (Fig. 5, E and F), while HFMs number and magnitudes decrease. These results suggest that at long distances, tactile information may be transmitted mostly through HFMs while at short distances, tactile information may be conveyed through WMs and HFMs. To test this prediction, we used LDA and compared classification scores using WMs, HFMs, and all parameters for the two distances. When calculating the classification scores using all variables, we found that if a surface is located distantly from the pad, the different whiskers transmit texture information less reliably and accurately. This is the result of a reduction in the contribution of WMs signals (Fig. 8C). Finally, to determine whether distance information may be inferred from vibration signals, we employed the same texture at different distances and used LDA on HFMs and WMs characteristics. We found that relative distance information can be inferred from the vibration signals. Examination of the percentage of correct classifications for all variables revealed that distance discrimination was correctly classified in 95% of whisks (n = 4).
Rats actively sweep their whiskers across surfaces in a rhythmic forward and backward motion with a frequency ranging from 5 to 25 Hz. To determine the influence of whisking frequency on texture discrimination, we induced artificial whisking at frequencies of 6 and 12 Hz. An example of whisker B2 responses to texture P150 at these frequencies is shown in Fig. 9A. Whisking frequency had a considerable influence on vibration signals transmitted by the whisker. The difference between the two frequencies was further confirmed by comparison between all studied variables in response to the same texture (n = 3; Fig. 8B; B2, P150). The panel shows that at higher whisking frequency, most variables were reduced in magnitude due to a smaller whisking trajectory.
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DISCUSSION |
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; Kleinfeld et al. 2006). Despite broad interest in this system and the consensus that whisker vibrations carry tactile information, most studies that analyzed whisker vibrations used passive whiskers stimulation (but see Arabzadeh et al. 2005). The present report provides a systematic analysis of whisker vibration signals during artificial whisking across textures in anesthetized rats. We discovered that tactile information is transmitted through high-frequency micromotions superimposed on whisking macromotions. Consistent with this, we found that in most TG neurons, spike activity and high-frequency micromotions are closely correlated. Despite a large variability in the translation process, different textures are translated into distinct vibration profiles. This translation can be mediated by all whiskers and is influenced by the mechanical properties of the whiskers, the radial distance of the surface, and whisking frequency. Hence in natural conditions, numerous factors influence texture-dependent whisker vibration signal as they arrive at the follicle and may influence texture discrimination. We propose that texture discrimination may require the integration of signals from multiple whisks and whiskers to disambiguate surface roughness. We tested this prediction by combining tactile information from multiple whisks and whiskers. Our results show that increasing the number of whisks and integration of information from multiple whiskers improved texture discrimination. The general picture that emerges from the present experiments is that although tactile information can be transmitted through a single whisker, multi-dimensional stimulus space may require spatiotemporal integration of several sensory channels. Methodological considerations
Before discussing the implications of these findings, it is important to consider the set of assumptions on which interpretation is based and the similarities and the differences between our results and those that were reported for awake behaving animals. The first concerns stimulus configuration. In awake whisking animals, the whiskers move rostrocaudally as they scrape against the surface of an object (Brecht et al. 1997; Carvell and Simons 1990; Hartmann 2001; Ritt et al. 2008). In the current study, whisker stimulation is done by having a flat surface contact a whisker. Textures are located so that plane of the surface is placed in parallel to the whisker axis. This configuration is a modified stimulus arrangement that was employed recently (Arbazadeh et al. 2005) and was chosen for the following reasons: first, under natural conditions, rats may contact a surface at the tip, in the middle, or close to the base of the vibrissa (Brecht et al. 1997; Hartmann 2001; Ritt et al. 2008) and they frequently sample textures with the vibrissae pressed against the sensory surface (Carvell and Simons 1990, 1995 observed typical contact points at 15 mm from the face, whereas Ritt et al. have seen contact points at 8–12 mm from the face). In several studies that investigated the translation of surface roughness into whisker vibrations, the vibrissae were pinned horizontally against a rotating surface with a contact point at 15 mm from face (Neimark et al. 2003) or were lying on a 3 x 3 mm surface 7 mm from the face (Arbazadeh et al. 2005). In contrast, we included artificial whisking and large surfaces at realistic distances. In some experiments in which only the tip made contact with the surface the vibrations transmitted to the shaft were diminished (see Fig. 8), and the duration of contact were changing from whisk to whisk. In these conditions, the whiskers were more prone to get stuck in grooves of the surface, resulting in a large variability in vibration signals from whisk to whisk (it was technically not feasible to view the tips of the whiskers and to place them in an accurate and precise location to create reliable responses). These problems may be alleviated in the awake animals by including head and body movements (Ritt et al. 2008). Second, although whisking is not restricted to a single plane of motion, it was claimed elsewhere that movement along the anterior-posterior axis contains most texture-specific signals (Arabzadeh et al. 2005). We have confirmed this in the current study, having found that dorsoventral motion carries only insignificant tactile information (Supplementary Fig. S4). Therefore we examined the principal direction of vibrissae movement, corresponding to the horizontal forward-backward axis. Third, artificial whisking generates whisking-like movement of the vibrissae. However, it should be kept in mind that the actual displacement generated by electrical whisking deviates from naturally occurring whisking. For example, whiskers movement is controlled by the facial motor nerve, which innervates two classes of muscles: intrinsic and extrinsic muscles. In awake animals, whisker retraction probably involves activation of extrinsic facial muscles (Berg and Kleinfeld 2003), whereas in our experimental paradigm, retraction is passive. Furthermore, artificial whisking results in movement of the whole pad and simultaneous activation of all muscle groups that work at odds with one another during protraction. While whisking movements during artificial and natural conditions may differ in detail, the range of HFMs magnitudes (Fig. 5A) (Ritt et al. 2008) and whisking amplitudes (Fig. 5E) (Berg and Kleinfeld 2003; Carvell and Simons 1990; Knutsen et al. 2005), which are well within the range reported in awake behaving animals, imply that the principles by which surface roughness is translated into whisker movement properties should be similar. Finally, in awake animals, whisking patterns vary across cycles (Carvell and Simons 1990; Gao et al. 2001) and between whisking bouts (O'Conner et al. 2002). Furthermore, whisker movements are actively controlled so as to increase the likelihood of environmental contacts (Knutsen et al. 2006; Mitchinson et al. 2007). During artificial whisking the contraction of whisker muscles is completely independent of contact. Hence although the basic pattern of movement trajectory during constant frequency bouts is preserved in our experiments, the details of movements and mechanical interactions probably differ.
Texture coding
Rats use their whiskers to discriminate surface roughness (Carvel and Simons 1990, 1995; Guic-Robles and Jenkins 1992; Guic-Robles et al. 1989). One model posits that different whiskers are tuned to different resonant frequencies, which vary systematically with whisker length along whisker rows and thus split the tactile vibration signals into labeled frequency lines in the cortex (Andermann et al. 2004; Hartmann et al. 2003; Mehta and Kleinfeld 2004; Moore 2004; Moore and Andermann 2005; Neimark et al. 2003). Our results do not rule out the possibility that rats utilize the gradient of resonant frequencies across the whisker array, as proposed by this theory. However, we did not observe an obvious gradient of prominent resonance frequencies across the whisker array as expressed in HFMs interval (Fig. 7B). This may be due to our stimulus configuration in which the whiskers were lying on the surface in contrast to the configuration reported by Neimark and colleagues (2003). The gradient of mechanical properties across the whisker array was evident in other characteristics such as the number and amplitude of HFMs. Moreover, altering these mechanical properties by trimming the whiskers leads to a reduction in texture-related events, amplitude and ITI, suggesting that whisker length plays a critical role in the translation of surface roughness to whisker vibrations (not shown). Finally, placing the surfaces at different distances modifies all aspects of vibration signals reaching the follicles (Fig. 7B) (see also Fig. 7A in Ritt et al. 2008), suggesting that the mechanical properties of whiskers play a critical role in the translation of surface roughness to whisker vibrations. Alternatively, it was shown that the mechanical properties of the vibrissae act to translate surface roughness into different intrinsic frequency or velocity modes for each vibrissa (Andermann et al. 2004; Hipp et al. 2006). The differences between surfaces are expressed by the extent to which the different modes are favored within each vibrissa (Fend et al. 2003; Mehta and Kleinfeld 2004). In line with these earlier findings, our results show that individual whiskers provide information about a broad range of textures and that whisker vibrations provide a potential cue that might be used for the accurate discrimination of tactile stimuli. These results do not rule out the use of resonance-based vibration signals comparisons across whiskers for texture discrimination; however, they suggest that a whisker should not be regarded as a band-pass filter for tactile information. Taken together, the findings of the current study indicate that although additional information might be available due to differences in the mechanical properties among whiskers, even a single whisker can transmit large amounts of texture-specific information to its downstream neural circuits.
While these models remain to be rigorously tested, potentially important properties of active whisking were neglected. First, under natural whisking conditions, the contact point of the whisker with the surface and the forces at the contact point are likely to change continuously, producing a dynamic modulation of the mechanical properties of the system and variable vibratory tactile information. Here the whiskers were moved forth and back across textures in an attempt to mimic the dynamics of natural whisking. However, changes in the distance of surfaces (Fig. 8) or repositioning of the surfaces at the same distance (not shown) resulted in a critical alteration in vibration signal and the reliability and accuracy in which whiskers transmit this information. Second, as rats whisk across object surfaces they continuously change the geometry of the whisker relative to the texture. They can change the amplitude, speed, and direction of motion (Mitchinson et al. 2007; Sachdev et al. 2001; Towal et al. 2008), producing ever-changing patterns of sensory inputs in different sets of fibers during the scan of regularly textured surfaces. We have tested these, in part, in the current study and found that vibration signals that transmit tactile information are dependent on the radial distance of the textures and on whisking frequency. Third, there is evidence for changes in whisking motor strategy according to the discrimination task. In general, rats vary their whisking amplitude and speed (Berg and Kleinfeld 2003), depending on task type (Carvell and Simons 1995), which, in turn, may modulate the mechanical properties of the whiskers. This fits well with our results which show that whisking frequency modulated all whisker vibration characteristics (Fig. 9). Finally, active whisking movements are always associated with head and body movements. When rats are engaged in a localization task, in which they estimate the location of objects relative to their head, they typically whisk (Knutsen et al. 2005). However, rats often rely solely on passive movement of the vibrissae secondary to body and head movements. Specifically, rats use their vibrissae but do not whisk as they maintain contact with walls and surfaces (Milani et al. 1989; Ritt et al. 2008) while running and while performing an aperture-size discrimination task (Krupa et al. 2001). Thus the vibrations induced in a whisker as it sweeps over a surface are likely to depend, in addition to the structure of the textured surface, on intrinsic and extrinsic variables such as the properties of active whisking (frequency, sweep amplitude, whisker mechanical properties), the distance between whisker shaft and the surface, etc.
Several studies have examined the spatial and temporal constraints on texture discrimination and object localization. These studies have argued that tactile information may be available through a single whisker (Carvel and Simons 1995; Hipp et al. 2006; Knutsen et al. 2006; Krupa et al. 2001) on the time scale of a couple of whisks (Mehta et al. 2007). It should be noted, however, that these studies have examined whisking behaviors during tactile discrimination tasks that necessarily require intense training of the animals and constrain the way in which the animal contacts the discriminandum. For example, single whisker tasks occur after the animals were heavily trained with all whiskers intact. Animals in single whisker tasks succeeded only in the easier discrimination tasks (Fig. 7 in Carvel and Simons 1995; Krupa et al. 2001). In contrast, other studies have shown that rodents employ several whisks for texture discrimination (von Heimendahl et al. 2007) and object localization (Knutsen et al. 2006) and in some experimental setups, animals used several whiskers for texture discrimination (Ritt et al. 2008).
We propose, based on our results and the literature (as discussed above), that due to a large variability in the translation of surface roughness to vibration signals, and the influence of numerous variables (see preceding text) on this translation, texture discrimination may require the integration of signals from multiple spatial and temporal sensory channels (whiskers, whisks, neurons). In this framework, the relay of sensory inputs may not actually rely on the frequency-following ability of individual TG neurons (Andermann et al. 2004; Jones et al. 2004) but rather on coordinated discharges among an ensemble of neurons having different response preferences. This conjecture is consistent with recent studies showing that individual TG neurons encode different aspects of tactile signals that may underlie object localization (Szwed et al. 2003, 2006), provide information about the movement of whiskers in the air and encode information regarding contact with an object (Leiser and Moxon 2007), and mediate the detectability of a single whisker deflection as a function of amplitude and peak velocity (Stüttgen et al. 2006). Thus the discrimination of behaviorally relevant tactile stimuli can be explained, in part, by dynamic mechanisms that integrate sensory information from multiple channels. For example, disambiguation of texture signals at different distances can be achieved when the animal can access information about its distance from the surface and its self-generated whisking motion. This information can be inferred from whisker vibration signal, and from distance signal (Ahissar and Kleinfeld 2003; Szwed et al. 2006) and whisking signal (Crochet and Petersen 2006; Fee et al. 1997; Gaunguly and Kleinfeld 2004; Kleinfeld et al. 2006; O'Connor et al. 2002). The distance signal is relayed by the sensory afferents. The whisking signal, in contrast, could originate either as an efferent copy relayed from the motor system to the sensory system (Ahrens and Kleinfeld 2004) or as a sensory signal from the whisker follicle (Szwed et al. 2003). In summary, the rat's knowledge of spatial coordinates and of its own motor output could make the sensory input more reliably decodable.
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ACKNOWLEDGMENTS |
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1 The online version of this article contains supplemental data. 
Address for reprint requests and other correspondence: R. Azouz, Dept of Physiology, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel 84105 (E-mail: razouz{at}bgu.ac.il)
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