sparse temporal coding of elementary tactile features during active whisker sensation

Sparse temporal coding of elementary tactile featuresduring active whisker sensation

Shantanu P Jadhav1,2, Jason Wolfe3 & Daniel E Feldman2

How the brain encodes relevant sensory stimuli in the context of active, natural sensation is not known. During active tactile

sensation by rodents, whisker movement across surfaces generates complex whisker micro-motion, including discrete, transient

slip-stick events, which carry information about surface properties. We simultaneously measured whisker motion and neural

activity in somatosensory cortex (S1) in rats whisking across surfaces. Slip-stick motion events were prominently encoded by one

or two low-probability, precisely timed spikes in S1 neurons, resulting in a probabilistically sparse ensemble code. Slips could be

efficiently decoded from transient, correlated spiking (~20-ms time scale) in small (~100 neuron) populations. Slip responses

contributed substantially to increased firing rate and transient firing synchrony on surfaces, and firing synchrony was an important

cue for surface texture. Slips are thus a fundamental encoded tactile feature in natural whisker input streams and are represented

by sparse, temporally precise, synchronous spiking in S1.

How sensory systems encode behaviorally meaningful stimuli duringactive, natural sensation is a central problem in neuroscience that canbe effectively studied in the tactile system1–3. Rat whiskers are highlysensitive tactile detectors3–5, similar to primate fingertips6, that areactively moved through the environment to extract information aboutobject position7,8, shape4 and surface features such as texture9,10.Whisker movement across objects creates dynamic input streams, butwhich features of these input streams are encoded by the nervoussystem and are relevant for perception are largely unknown3,11. For thecase of surfaces, whisker motion generates complex micro-motion,including discrete, transient slip-stick events driven by frictional inter-actions with the surface. Slip-stick events have been observed in avariety of behavioral conditions and carry information about surfaceproperties12–14. However, whether and how natural slip-stick events areencoded in the brain are unknown.

Passively applied whisker deflections and active whisker contactevents evoke low-probability responses in primary somatosensory(S1) cortex15–19. Sensory coding in S1 has therefore been proposed tobe sparse4 and sparse activation of small numbers of S1 neurons to bebehaviorally detectable20,21. This type of sparse coding, which resultsfrom low response probability, is distinct from the predominant sparsecoding strategy proposed for V1 and A1, which is based on strongand reliable single-unit responses to a narrow range of preferredstimuli22–24. Whether complex, temporally dense whisker inputstreams during active whisking on surfaces are also sparsely encodedis not known.

To address these issues, we simultaneously measured whisker micro-motion and neural activity in S1 in actively behaving rats trained towhisk on textured surfaces. We found that discrete slip-stick events

(which we refer to as slips) were encoded by S1 neurons with sparse,low-probability, precisely timed spikes during continuous whisking onsurfaces. Temporally precise slip-evoked responses drove transientcorrelation between S1 neurons. These transient correlations encodedslips very efficiently, provided higher signal-to-noise detection ofsurfaces than slower modulation of firing rate and were an importantcue for surface texture. Thus, whisker slips are fundamental encodedfeatures of natural whisker input streams and are encoded by a sparsecoding strategy, which is based on probabilistic responses in neuralensembles, rather than on narrow stimulus selectivity.

RESULTS

Slips are prominent during active whisking on surfaces

We trained freely behaving rats with one intact whisker to position theirnose in an aperture (nose poke) and whisk across sandpaper surfaces orin air while we simultaneously recorded whisker motion and spiking incontralateral S1 (Fig. 1a,b and Supplementary Video 1 online).Whisker motion was measured with a high-resolution CCD (charge-coupled device) imaging array (B5-mm and 0.25-ms resolution)12.

Superimposed on slow, continuous, 5–12-Hz whisking motion3–5 onsurfaces were dense sequences of discrete slips, which are known to beinduced by whisker interaction with surfaces12 (Fig. 1c,d and Supple-mentary Fig. 1 online). Slips have been hypothesized to be elementaryfeatures of active whisker input and to convey information aboutsurface properties12,13. Slips consisted of transient (B2 ms), high-velocity, high-acceleration movements that were often followed by briefoscillatory ringing of the whisker (Fig. 1c) and occurred during bothforward (protraction) and backward (retraction) motion (Fig. 1c andSupplementary Fig. 1). Acceleration and velocity of slips were highly

Received 28 October 2008; accepted 6 April 2009; published online 10 May 2009; doi:10.1038/nn.2328

1Computational Neurobiology Graduate Program, University of California, San Diego, La Jolla, California, USA. 2Department of Molecular and Cellular Biology and HelenWills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA. 3Department of Physics, University of California, San Diego, La Jolla, California,USA. Correspondence should be addressed to D.E.F. ([email protected]).

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correlated (r ¼ 0.83 ± 0.01, mean ± s.e.m., range ¼ 0.72–0.92, n ¼ 22recording sites, P o 0.01 for all recording sites, t statistic), as reportedpreviously12. Whisker motion in air was smoother, with fewer high-acceleration peaks (Fig. 1d and Supplementary Fig. 1).

Slips were detected as acceleration transients that surpassed definedacceleration thresholds (Y). Thresholds were determined from themeasured distribution of acceleration on surfaces (see Online Methods)with Y ¼ 0.32 mm ms–2, corresponding to four standard deviationsabove mean acceleration, used unless otherwise noted (Fig. 1d).Analysis was confined to initial slips in slip-ring sequences (first slips,defined as slips with no prior acceleration threshold crossing in 20 ms).First slips occurred frequently (median interslip interval ¼ 62 ms,25th–75th percentile ¼ 40–99 ms) and had high peak velocity (0.2–1.2mm ms–1, corresponding to B300–1,900 s–1) and acceleration (0.2–1mm ms–2), suggesting that they drive spikes in vivo15,16,25.

Slips drive sparse, precisely timed spikes in S1 neurons

To determine whether slips are encoded in S1, we made neurophysio-logical recordings using a chronic, moveable tetrode implanted in theS1 column corresponding to the intact, imaged whisker4,5,15,16

(Fig. 2a). The whisker corresponding to the recorded column (the

principal whisker) was determined from spiking responses to whiskerdeflection under anesthesia, and all but that whisker (D1, D2 or E1)were trimmed. Single-neuron spike trains were isolated by spike sorting(see Online Methods26; Fig. 2b–d and an additional example is shownin Supplementary Fig. 2 online). In all, 90 single units were isolatedfrom 22 recording sites in four cortical columns of three rats (2–6 unitsper site, mean of 4.1). Recording sites were located in cortical layers (L)4 and 5, as determined by recording depth27, and later confirmed byhistology relative to cytochrome oxidase staining.

First slips evoked sparse, but precisely timed, spikes in most S1neurons, as revealed by rasters and peristimulus time histograms(PSTHs) aligned to first slips across multiple whisking trials(Fig. 3a,b). PSTHs aligned to all slips (including both first andsubsequent slips and rings) showed weaker, more temporally dispersedresponses (Fig. 3c). For each neuron, we fit the first slip-aligned PSTHwith a Bayesian adaptive regression splines (BARS) algorithm28

(Fig. 3b). We found statistically significant slip-evoked response peaksin 62 out of 90 (B70%) neurons (P o 0.05, as determined by confi-dence intervals from the fit). Response magnitude was quantified usinga slip response index (SRI) representing the percent increase above thepre-slip baseline firing rate of the response peak. Slip-responsive

neurons (n ¼ 62) had a mean SRI of 141.8 ±13.3%, short latency to peak response (8.3 ±0.6 ms) and low jitter (13.0 ± 1.2 ms, full widthat half maximum of the response peak)(Fig. 3d). Thus, slip timing is encoded in S1with B20-ms resolution. A separate, multipleregression analysis29 revealed that whiskeracceleration and velocity in B20-ms temporalwindows (that is, the kinetic parameters thatdefine slips) were significant predictors ofinstantaneous spiking probability in slip-responsive neurons, whereas whisker positionwas not (P o 0.05; data not shown). Thus,slips are a basic encoded element in naturalwhisker input streams.

Some first slips are rapidly followed by oneor more oscillations (rings) at the whisker’sresonance frequency12. Rings have been sug-gested to amplify sensory responses30. Wefound that rings modestly, but significantly,increased responses to slips, consistent withthis hypothesis (P ¼ 0.01, t test; Supplemen-tary Note and Supplementary Fig. 3 online).

Slips contribute to surface-driven spikes

S1 neurons show sparse background firing inawake rodents18,19,31 and modest firing-rateelevations during surface palpation32,33 andother active sensory tasks34. We found averagefiring rates, calculated over entire recordingsessions (30 min to 2 h, dominated by non-whisking epochs), of 0.3–48 Hz (mean, 7.5 ±0.8 Hz (s.e.m); median, 5.7 Hz; n ¼ 90neurons; Supplementary Fig. 4 online). Dur-ing whisking on surfaces, 69% of neurons(62 of 90 neurons) significantly increasedfiring rate (measured over the entire whiskingepoch) relative to pre-whisk baseline (t test,P o 0.05 criterion, 65–365 trials per neuron,median ¼ 160; Fig. 4a,b). These were termed

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Figure 1 High-acceleration, high-velocity whisker slips are prominent during active whisking on surfaces.

(a) Behavior and recording apparatus to simultaneously measure whisker motion and spiking of S1neurons during active surface palpation. (b) Sequence of events in a single behavior trial. A noise cue

was delivered on completion of a criterion number of whisks, signaling reward availability. t, variable-

length intervals. (c) Example of whisker motion on P150 sandpaper (boxes are first slips detected by

acceleration threshold crossings). Gray time period is expanded on the right. (d) Peak acceleration and

velocity of all slip events in one behavior session for interleaved trials in air and on P150 surface. Red

and green lines indicate 95th percentile values for whisking in air. Blue line represent 0.32 mm ms�2

acceleration threshold for detecting slips.

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surface-excited neurons. We found that 11% of the neurons (10 of 90)were significantly surface inhibited (Po 0.05 criterion) and 20% (18 of90) were surface nonresponsive (this latter class included somecells with onset responses followed by inhibition; Fig. 4b and Supple-mentary Fig. 4). On average, surface-excited neurons increased theirfiring rate by a factor of 2.21 ± 0.13 during whisking epochs. In a subsetof surface-excited neurons tested with interleaved trials on surfaceand air (n ¼ 15 neurons, three recording sites), the firing rateincreased during whisking on surfaces, but not during whisking inair (Fig. 4c). Thus, surface interactions, not motor commands orsensory reafference from whisker self-motion, drive increased firingduring surface whisking.

We assessed whether slip-evoked responses contribute to surface-induced changes in firing rate. Across neurons, SRI correlated with themagnitude of firing rate increase on surfaces (r ¼ 0.43, P o 0.001;Fig. 4d). Moreover, the firing rate in 40-ms periods during surfacewhisking was significantly correlated with the number of slips in thoseperiods for B75% of slip-responsive neurons (n ¼ 42 of 56 neuronswith sufficient trial length, P o 0.05 criterion, all slips exceeding 0.16mm ms–2 acceleration threshold, corresponding to 2 s.d. above themean, were used in this analysis; see Online Methods and Fig. 4e). Toestimate the fraction of surface-driven spikes that were attributable toslips, we calculated for each slip-responsive neuron the number ofspikes in 20-ms response windows following all first slips, and com-pared it with the total number of spikes in the same trials (only the first250 ms of each trial was considered). On average, 3.4 ± 0.05 slips

occurred per 250-ms trial (n ¼ 56 neurons, 40–235 trials per neuron).Slip-response windows contained 1.02 ± 0.18 spikes per trial, whichconstituted 34.8 ± 1.4% of the 3.07 ± 0.6 total spikes per trial. This wassignificantly higher than the 0.52 ± 0.07 spikes, or 19.0 ± 1.3% of totalspikes, that occurred in an equivalent number of non-slip epochs thatwere randomly chosen from the same trials (t test, P ¼ 0.012; Fig. 4fand Supplementary Fig. 5 online). Thus, first slips drove B16% of thetotal spikes that occurred during surface whisking. Because only abouthalf of the total spikes are driven by surface interactions (whisking onsurfaces increases firing rate 2.2-fold over pre-whisk baseline), firstslips account for B30% of the surface-driven increase in firing rate.Although this is not the majority of spikes, these slip-driven spikes mayencode important information about whisker slips and related stimuli.

Slip responses are sparse and probabilistic

Responses to passive whisker deflection in anesthetized and awake ratsevoke low-probability spiking responses15–17,19. Similarly, we found thatslip responses during active whisking on surfaces were sparse andprobabilistic, occurring only in response to a small fraction of slips,even for highly slip-responsive neurons (for example, SRI ¼ 296 and449, and 164 for Figs. 3a and 5a, respectively). Across all slip-responsiveneurons, only 24.0 ± 2.1% of first slips (defined as exceeding Y ¼ 0.32mm ms–2) were associated with spikes in the subsequent 20 ms, ascompared with 15.4 ± 1.9% of pre-slip (background) epochs (althoughmodest, this was a highly significant increase, P o 0.001, t test). Whenslip responses did occur, they were nearly always single spikes, and

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Figure 2 Recording configuration and single-unit sorting using tetrodes. (a) Spiking activity was recorded using chronically implanted, moveable tetrodes in

an ultra-miniature microdrive. Left, schematic of the implanted microdrive. Right, cytochrome oxidase–stained across-row section (see Online Methods)

showing a marking lesion and reconstructed recording track (red crosses). Barrels are visible in L4. (b) Example tetrode recording (850 mm, layer 4) showing

raw signal on all four tetrode channels (Ch1, Ch2, Ch3 and Ch4). Spikes of three isolated single neurons are denoted by circles at bottom. (c) Amplitude plots

for the three isolated units from b. Amplitudes were stable throughout the recording session. (d) Density plots of spike waveforms for these three units (E1–4

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occasionally doublets, similar to spontaneous firing during pre-whiskepochs (Fig. 5b,c). On the ensemble level, net spike probability (meanresponse probability in the 20 ms after each slip – that before the slip)measured across all 90 recorded neurons was 0.09 ± 0.01 for Y ¼ 0.32mm ms�2 and increased to 0.13 ± 0.01 for the largest slips (Fig. 5d).Thus, slips generate sparse, low-probability, one- or two-spike responsesin single S1 neurons and modest, transient elevations in firing rate onthe ensemble level. We could not detect any difference in responseprobability between initial and subsequent slip events occurring insingle trials, indicating that sparse responses are not the results ofadaptation accruing during slip trains (data not shown).

Low response probability could reflect either probabilistic respon-siveness or tuning for whisker kinetic properties that vary from slip toslip. S1 and thalamic neurons are sensitive to whisker-deflectionvelocity15,16,25,35 and acceleration36 and are weakly tuned for directionof deflection25,37. In awake animals, neurons are also modulated byposition in the whisking cycle (phase)38. To test whether slip accelera-tion (or velocity, which is highly correlated with acceleration) isencoded, we calculated slip-evoked PSTHs from slips that wereidentified with increasing acceleration thresholds12. The net spikingprobability increased with increasing acceleration threshold, indicatingthat high-acceleration (and high-velocity) slips were most stronglyencoded by single neurons (Fig. 6a). However, even the highestacceleration slips (Y¼ 0.80 mm ms�2, 10 s.d. above the mean) evokedresponses on only a fraction (B0.13) of trials (Fig. 5d). Across allneurons, the lowest acceleration threshold that elicited a significantslip-evoked response (P o 0.05 criterion, termed the response thresh-old) was 0.17 ± 0.01 mm ms–2 (n ¼ 62, mean ± s.e.m.; Fig. 6b), whichclosely matched the acceleration of the weakest slips evoked on surfacesrelative to air (Fig. 1 and Supplementary Fig. 1). Thus, S1 neuronsencode even the weakest surface-relevant slips. Neurons varied widelyin gain, defined as the fold-change in peak firing probability per two

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Figure 4 Slips drive a substantial fraction of spikes during surface whisking.

(a) PSTH (20-ms bins) of firing of one surface-excited neuron, aligned to

whisking onset. Lick onset indicates the onset of licking for water reward in

the lick port. The orange line indicates the baseline (average) firing rate. NP,

nose poke. (b) Mean PSTHs (25-ms bins, normalized to pre-whisk firing rate)

for the three response classes during surface whisking. The colored regions

represent 95% confidence intervals. ** P o 0.01. (c) Mean PSTHs (50-msbins) for 15 neurons during interleaved trials of surface whisking and whisking

in air. (d) Correlation between SRI and increase in firing rate during surface

whisking (t statistic, P o 0.001). (e) Example neurons illustrating correlation

between number of spikes (mean ± s.e.m.) and number of all slips in 40-ms

windows (t statistic, P o 0.01 for all three neurons). Regression lines are

overlaid. The colored bars on the left denote the average firing rate of neurons.

(f) Mean spike counts in slip and nonslip epochs per trial, compared with total

spike count per trial, for slip-responsive neurons (n ¼ 56).

Figure 3 Slips drive sparse, precisely timed spikes in S1 neurons.

(a) Spike rasters for two slip-responsive neurons aligned to first slip events.

(b,c) PSTHs (2-ms bins) aligned to first slips (b) and all slips (c) for the

neurons shown in a. Blue curve indicates BARS fit with confidence intervals.

Orange line denotes the mean firing rate of the neuron averaged over the

entire recording session. (d) SRI versus depth for all neurons. Red indicates

slip-responsive neurons. Right, response latency and jitter for all

slip-responsive neurons.


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standard deviations (0.16 mm ms–2) of acceleration (mean gain¼ 1.5 ±0.1, n ¼ 62; Fig. 6b).

To determine whether S1 neurons were tuned for slip direction orwhisking phase (which covary during natural whisking), we com-pared responses to forward slips, which occur during protraction,and backward slips, which occur during retraction. Comparisonswere restricted to slips of matched acceleration (see Online Methodsand Supplementary Fig. 6 online). For low-acceleration slips,relatively few neurons (13 of 62) showed a significant preferencefor direction/phase (nonparametric one-way ANOVA for eachneuron, P o 0.05 criterion) and the magnitude of selectivity wasweak (modulation index ¼ [protraction response minus retractionresponse]/average response, mean |modulation index| ¼ 0.57 ± 0.10,n ¼ 62), similar to a previous study in L2/3 of anesthetized rats17

(Fig. 6c,d). Identical results were observed for higher accelerationslips (Supplementary Fig. 6). Thus, selectivity for slip direction andwhisking phase was weak, as has been predicted previously39.Finally, we tested whether S1 neurons were tuned for slips occurringat a particular absolute whisker position. Slips were divided intofour interquartile ranges of absolute position (calculated from alltrials at a recording site, Supplementary Fig. 6). We found nosignificant modulation of slip response amplitude by whisker posi-tion (measured at slip onset, nonparametric one-way ANOVA foreach neuron, P o 0.05 criterion; Fig. 6e).

Thus, neither tuning for acceleration/velocity, slip direction/phase orabsolute position can account for the observed low-probability slipresponses. We therefore conclude that slips elicit probabilistic, infre-quent responses in single neurons. Because these responses are indis-tinguishable in single trials from background firing, slips are probablyencoded in ensemble activity of S1 neurons.

Population representation of slips

To determine whether slips are detectablefrom sparse population activity in single trials,we simulated single-trial ensemble responsesto slips using neural response profiles drawnfrom all 90 recorded neurons. In each iteration(one slip), we constructed the ensemble res-ponse by assigning 0 or 1 spikes to a pre-slip(background) or post-slip response window(20-ms window size) for each neuron on thebasis of the neuron’s empirically measured

spike probabilities for slips exceeding a specific acceleration thresholdand by summing the total spikes of the population in each timewindow (Fig. 7a). This gives the instantaneous population responseto a slip in the time window. This model assumes independencebetween neurons, which is consistent with low firing correlationsseen between L2/3 neurons in awake S1 (ref. 31) and with the jointspiking probability of neuron pairs in L4 and L5 measured in ourexperiments (see below). The accuracy of detecting a slip from thesingle-trial response was quantified using receiver operating character-istic (ROC) analysis40 from distributions of ensemble spike counts (100iterations) for background and post-slip windows (SupplementaryFig. 7 online).

Varying the simulated population size to generate a populationneurometric curve revealed that a B100 neuron population can detecta large slip (Y ¼ 0.8 mm ms–2) with 495% accuracy (Fig. 7b). Theoptimal window size for detecting a slip was 20 ms, independent ofpopulation size (Fig. 7c). Large slips were more robustly encoded at allpopulation sizes and 400 neurons were sufficient to detect even thesmallest of slips (Y¼ 0.16 mm ms–2) with 495% accuracy (Fig. 7d,e).Thus, sparse activation in a population of a few hundred S1 neurons ina brief, 20-ms time window efficiently encodes the occurrence of slips.This constitutes a temporal code for slips on the basis of transient,stimulus-induced elevation in population firing rate, leading toincreased firing correlations between neurons.

Coding of slips and surface texture by synchronous firing

This finding predicts that slips are encoded by transient increase infiring synchrony over background spiking, which is known to be sparseand relatively poorly correlated in S1 (at least in L2/3)17,31,41. Here,synchrony denotes raw firing correlations, not adjusted for correlations

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Figure 5 Slip responses are sparse and

probabilistic. (a) First slip (Y ¼ 0.32 mm ms–2)

aligned raster for a representative neuron (average

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sparse responses to a small fraction of slips.

(b) Percentage of pre-slip (background) and

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containing 0, 1, 2 or 42 spikes for all 62 slip-responsive neurons (overlaid: median ± inter-

quartile range). (c) Spike raster and instantaneous

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a neuron (firing rate, 4 Hz) across 5 trials in one

behavioral session. Gray areas: 1,500-ms behavior

period, starting at nose-poke onset. (d) Net spike

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threshold. Inset: Mean net spike probability in the

population for each threshold (mean ± s.e.m.).

* P o 0.05, ** P o 0.01.


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resulting from independent firing. In fact, joint spiking probability(total correlated firing) for neuron pairs after slips was equal to theproduct of the individual spiking probabilities in the population(Supplementary Fig. 8 online), indicating that L4 and L5 neuronsfire largely independently during active whisking, as reported for L2/3neurons31 (that is, internal correlations are low). Consistent with thepredictions of the model, synchronous firing of simultaneouslyrecorded neuron pairs in vivo was significantly greater in the 20-mswindow after slips than before (ratio ¼ 2.34 ± 0.17, P ¼ 0.029; Fig. 8aand Supplementary Fig. 9 online) and higher-acceleration slips drove agreater increase in response synchrony than lower-acceleration slips(Fig. 8a). To determine whether this measurement of firing synchronywas biased by the inability to detect exactly simultaneous spikes(o1-ms interspike interval, ISI) on single tetrodes, we estimated thenumber of undetected simultaneous spikes (0-ms delay) from spikes at±1-ms delay in cross-correlation histograms42. Inclusion of theseundetected spikes had no effect on our measures of increased firingsynchrony following slips or independence between units (Supple-mentary Fig. 8).

Because slips occur on surfaces, firing synchrony should be elevatedon surfaces. Indeed, firing synchrony for neuron pairs (measured incascading 20-ms bins, without knowledge of whether or when slipsoccurred) provided a higher signal-to-noise measure of whisking onsurfaces, relative to pre-whisk baseline, than individual neuron firingrates (t test, P o 0.001; Fig. 8b). The increase in synchronyduring surface whisking matched that expected from the productof individual neuron firing rates, confirming that firing rate increased

independently between neurons (t test, P ¼ 0.653; Fig. 8b). Thus,synchronous activation of S1 neurons provides a robust code fordetecting surfaces.

Because the statistics of slips vary with texture12–14, slip-evoked firingrate and synchrony may be cues for texture. At two recording sites, weconfirmed that a rough surface (P150 sandpaper, 228 trials) elicited agreater rate of high-acceleration slips than a smooth surface (polishedaluminum, 165 trials) or whisking in air (171 trials) (P o 0.01, two-way ANOVA; Fig. 8c). The mean firing rate (n ¼ 9 neurons) wassignificantly higher on the rough surface (9.4 ± 0.8 Hz) than on thesmooth surface (7.3 ± 0.7 Hz, P ¼ 0.03, Kolmogorov-Smirnoff test)and was higher on the smooth surface than in air (4.6 ± 0.9 Hz, P o0.01, Kolmogorov-Smirnoff test), as was seen in rats discriminatingtextured surfaces33 (Fig. 8d). Synchronous firing in pairs of neurons(n ¼ 16 pairs, measured in cascading 20-ms bins) showed an evengreater difference with texture (rough, 4.0 ± 0.5 synchronous spikes pers; smooth, 2.3 ± 0.3; air, 1.0 ± 0.2; all Po 0.01, Kolmogorov-Smirnofftest; Fig. 8e). The utility of a firing synchrony code for texture wasespecially apparent during whisking on four similar textures in inter-leaved trials (P150, P400, P800 and P1200 sandpapers, n ¼ 195, 151,173, 160 trials, respectively). High-acceleration slips were more com-mon on P150–800 than on P1200 surfaces, as reported previously12

(Fig. 8f). Although the mean firing rate (n ¼ 17 neurons, fourrecording sites) was not different between these surfaces (7.1 ± 1.3Hz, 7.0 ± 1.3 Hz, 6.5 ± 1.2 Hz and 6.0 ± 1.2 Hz for P150, P400, P800and P1200 sandpapers, respectively; P Z 0.19 for all comparisons,Kolmogorov-Smirnoff test), firing synchrony in neuron pairs (n ¼ 28pairs) was systematically stronger on the rougher surfaces (3.3 ± 0.5,3.1 ± 0.5, 2.8 ± 0.6 and 2.1 ± 0.5 synchronous spikes per s, respectively;P o 0.01 for P150 versus P1200 and P ¼ 0.04 for P400 versus P1200,Kolmogorov-Smirnoff test; Fig. 8g,h). This difference in firingsynchrony between textures was not seen on the 100-ms time scale(8.3 ± 1.7, 7.7 ± 1.6, 8.6 ± 1.8 and 6.7 ± 1.4 synchronous spikes per sfor P150, P400, P800 and P1200 sandpapers, respectively; P Z 0.17for all comparisons, Kolmogorov-Smirnoff test; SupplementaryFig. 10 online).

DISCUSSION

Our results indicate that slips are fundamental encoded elements ofwhisker input streams during active, natural whisking on surfaces. Slipresponses accounted for a substantial fraction of surface-driven spikes(B30%). Slips were encoded with high temporal precision by syn-chronous, but low-probability, spikes on a background of sparse,poorly correlated firing. Thus, natural whisking transforms spatiallycontinuous tactile stimuli (surfaces) into a stream of discrete, high-acceleration/high-velocity elements (slips) and represents these

0 1 2 30

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Figure 6 Encoding of slip properties. (a) Slip-acceleration response plot for

an example neuron. Each row of the matrix is the normalized first slip-locked

PSTH at one acceleration threshold. The horizontal black line denotes the

response threshold of the neuron. (b) Distribution of response threshold and

gain in the population. (c) Example neurons with selective responses for

direction of motion, protraction (left) and retraction (right). The mean

acceleration of protraction slips was 0.239 ± 0.004 mm ms–2 (n ¼ 450

slips), and the mean acceleration of retraction slips was 0.240 ± 0.003 mmms–2 (n ¼ 832 slips). (d) Distribution of direction-selective neurons

(retraction selective, green; protraction selective, magenta) in the population.

Inset, distribution of modulation indexes. Neurons with significant direction-

selective responses (P o 0.05 criterion, nonparametric one-way ANOVA for

each neuron) are indicated by colored bars. (e) Distribution of slip responses

in the population on the basis of absolute position. Significance for each

neuron was assessed by nonparametric one-way ANOVA, P o 0.05 criterion.


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elements by temporally precise population activity in the correspond-ing cortical column in S1. Precise spike timing, which is relevant forcoding of sensory events with an explicit temporal structure, such ascontact with an object18,43, may therefore be crucial for encoding ofsurface properties.

Temporally precise slip responses drive transient synchronous firingof S1 neurons, resulting in a synchrony code for slips. This type ofsynchrony coding does not involve any increase in synchrony beyondthat expected from independent firing probability, unlike synchronycoding models in V1 (ref. 44), and is instead a straightforwardconsequence of rapid firing-rate modulation that should be readilydetectable, in principle, by downstream cells or networks that spatiallyintegrate across multiple S1 neurons in narrow time windows. Detec-tion of synchronous responses to slips may be facilitated by decorrela-tion of background synaptic activity during whisking31. Increased rawfiring correlations after slips (on the 20-ms time scale) provide a highersignal-to-noise measure of slips and surfaces than mean firing ratealone or than firing correlation on the 100-ms time scale. Thus,population coding of slips occurs on a shorter time scale than codingby firing-rate modulation during tactile flutter sensation in primateS1 (ref. 1).

One goal of this study was to test whether low-probability sensoryresponses, as have been observed many times in anesthetized S1 (refs.15–17,25) and in response to passive stimulation in awake animals19,also occur during active whisker sensation of complex, natural stimuli.

We found that whisker slips evoked low-probability responses during active whisking(B0.10), leading to a small, varying set ofactive neurons for each slip. Although suchlow-probability responses could be generateddeterministically by hidden stimulus selectiv-ity for kinetic properties that vary across slips,we failed to detect neuronal selectivity foracceleration, direction, phase or position thatcould account for the sparse responses.

Instead, we hypothesize that responses to individual slips were deter-mined probabilistically by low intrinsic firing probabilities of S1neurons, resulting in a small number of total S1 spikes per slip. This‘probabilistic sparse coding’ is distinct from sparse coding that has beenproposed in awake visual and auditory cortex or hippocampus, inwhich neurons respond highly selectively to small subsets of stimuli,but with large increases in firing rate23,24,45. True firing probability isprobably even lower than our data indicate, as a result of the many S1neurons that spike too infrequently to be detected by extracellularrecording4,17,18. High signal-to-noise encoding of slips and surfaces waspossible despite low spike numbers by detection of stimulus-inducedfiring synchrony.

In S1, assuming 4,200 neurons per column in L4 and L5 (ref. 46), amoderate whisker slip will elicit a single spike from B420 neurons,which is sufficient for robust behavioral detection20,21. Probabilisticsparse coding may represent a means of achieving the metabolic andcomputational advantages of sparse coding22 in systems with lowstimulus selectivity and may minimize synaptic and spiking adaptationduring temporally dense sensory streams, similar to the volley principlein the auditory nerve47.

Because the rate and magnitude of slips vary with surfacetexture12–14, slip-evoked firing synchrony may code for surface texture.Consistent with this hypothesis, rougher surfaces evoked greater firingsynchrony in neuron pairs than did smoother surfaces or whisking inair, and the signal-to-noise ratio for synchronous firing was greater

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Figure 7 Slips are efficiently represented by

transient synchronous activity in small neuronal

ensembles. (a) Schematic of simulation design,

with simulated ensemble response for a single

slip. Spikes were assigned to 2-ms time bins for

display only. The simulation was run on total spike

counts in the entire 20-ms background and

response windows. (b) Probability of correctdetection of a slip as a function of population size

on the basis of ROC analysis. Inset shows the

distributions of spike counts in background and

response windows for the 100-neuron population

size. (c) Probability of correct detection as a

function of time window used in the simulation

and population size. (d) Population neurometric

curves for varying slip amplitudes and population

sizes, using 20-ms windows. Each curve shows

the probability of correct detection from ROC

analysis. (e) Accurate detection of small slips in a

400-neuron population. Distributions correspond

to the gray bar in d. Each curve shows the

distribution of total spike number in the popu-

lation (20-ms time window) for 100 iterations for

the background epoch (black trace) and for slip-

response epochs for corresponding acceleration

threshold. Mean values of distributions are

denoted by dotted lines. All distributions werewell-separated from background.


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than for mean firing rate. Our results confirm that firing rate variesbetween highly distinct textures33 but indicate that for closely relatedtextures firing correlations on the 20-ms time scale provide a morerobust code than mean firing rate12,14. This synchrony-based coding oftexture may complement firing rate–based codes for large texturedifferences33, and additional codes may also be available3,14.

METHODS

Methods and any associated references are available in the onlineversion of the paper at http://www.nature.com/natureneuroscience/.

Note: Supplementary information is available on the Nature Neuroscience website.

ACKNOWLEDGMENTSWe thank P. Martin and S. Pahlavan for assistance with behavioral training,and M. DeWeese, B. Olshausen and Y. Dan for comments on an earlierversion of the manuscript. This work was supported by a National ScienceFoundation Integrative Graduate Education and Traineeship fellowship and aBurroughs Wellcome La Jolla Interfaces in Science fellowship (S.P.J.), by NationalScience Foundation Faculty Early Career Development Award IOB-0546098,National Science Foundation grant #SBE-0542013 to the Temporal Dynamicsof Learning Center and a University of California, San Diego HeiligenbergProfessorship (D.E.F.).

AUTHOR CONTRIBUTIONSS.P.J., J.W. and D.E.F. designed the experiments. S.P.J. and J.W. performed theexperiments. S.P.J. and D.E.F. analyzed the data and wrote the paper. All of theauthors discussed the results and commented on the manuscript.

Published online at http://www.nature.com/natureneuroscience/

Reprints and permissions information is available online at http://www.nature.com/

reprintsandpermissions/

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a

0 5 10 15 200

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Percentage of slips with synchronous spiking

Before slip (%)

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Time from whisk onset (ms)

Observed synchrony rateExpected synchrony rateFiring rate—neuron 1Firing rate—neuron 2

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Figure 8 Coding of slips and surfaces by synchronous firing. (a) Synchronous spiking in simultaneously recorded neuron pairs in vivo in 20-ms windowsbefore and after slips (n ¼ 63 pairs, mean ± s.e.m. overlaid). Right, synchrony ratio (after slip:before slip) as a function of slip acceleration threshold

(mean ± s.e.m.). (b) Firing rate and synchronous firing probability (20-ms bins) before and during surface whisking for a pair of simultaneously recorded

neurons. The blue trace denotes expected synchrony for independent firing of neurons. Inset, signal to noise for firing rate and synchrony during surface

whisking, relative to pre-whisk baseline (mean ± s.e.m., n ¼ 56 neurons, n ¼ 63 pairs). The blue bar indicates the expected synchrony for independent

neurons. (c–e) Distribution of frequency of slips of different magnitudes (c), mean firing rates of neurons (mean ± s.e.m., n ¼ 9 neurons, two recording sites,

d) and number of synchronous spikes per s in pairs of neurons (mean ± s.e.m., n ¼ 16 pairs, two recording sites, e) during whisking on P150 surface, smooth

surface and whisking in air. (f–h) Distribution of frequency of slips of different magnitudes (f), mean firing rates of neurons (mean ± s.e.m., n ¼ 17 neurons,

four recording sites, g) and the number of synchronous spikes per s in pairs of neurons (mean ± s.e.m., n ¼ 28 pairs, four recording sites, h) during whisking

on P150, P400, P800 and P1200 surfaces.


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ONLINE METHODSAll procedures were approved by the University of California San Diego and

University of California Berkeley Institutional Animal Care and Use Commit-

tees. Subjects were adult female Long Evans rats.

Behavioral training. Briefly, the behavioral apparatus12 consisted of an outer

reward chamber containing a solenoid-gated drink port and an inner task

chamber containing the nose poke, surfaces and whisker imaging system. Water

was delivered on completion of a criterion number of whisks. Surfaces

consisted of 6 � 6-cm pieces of sandpaper glued to an aluminum plate and

positioned parallel to the face at a distance of 5 mm from the tip of the intact

whisker12 (B35 mm from the follicle). Surface and air trials were interleaved

using a stepper motor to rotate a four-arm stimulus holder when the rat was

drinking in the reward chamber. For comparison of multiple textures, surfaces

were interleaved in blocks of five trials. For slip analysis, surface trial data were

pooled across trials using P150 grade sandpaper (most trials) and smoother

grades (P400, P800 and P1200 for a minority of trials).

Whisker imaging. Motion of the intact right-side whisker was imaged in the

front-back direction (parallel to the surface) by casting shadows of the whiskers

with a plane of laser light onto a linear CCD imaging array12. The CCD array

was positioned 2 mm from the surface. For each trial, the relationship between

whisker movement and spiking was analyzed only for the continuous period

during which the rat remained in the nose poke and whisker shadow remained

visible in the CCD frame (maximum of 1,500 ms after nose poke onset). This

ensured that the whisker remained in constant contact12 with the surface while

spiking was analyzed. Disappearance of the whisker shadow from the imaging

plane, implying loss of contact with surface, terminated the trial.

Chronic recording and spike sorting. For anesthesia, we used a combination

of ketamine and xylazine (100 and 20 mg per kg of body weight, respectively)

that was injected intraperitoneally and maintained with isofluorane (0.5–4%).

During a sterile surgical procedure, a tetrode microdrive48 with independently

moveable tetrodes was mounted with dental acrylic over a 4-mm diameter

craniotomy over the left posteromedial barrel subfield in S1 (5.5 mm lateral, 2.5

mm caudal to bregma). A 0.5-mm diameter silver reference wire was inserted

B2 mm into the opposite hemisphere. After sealing the microdrive, tetrodes

were advanced through intact dura with vacuum-assisted penetration48, until

spikes were observed. After 5–7 d of recovery, recording began. All but the

whisker (D1, D2, or E1) corresponding to the recorded tetrode were trimmed

at the base and re-trimming was performed weekly. Only recording sites with a

clear, unambiguous principal whisker were chosen. Recording sessions (1–2 per

d, 7–16 d per rat) lasted 30–120 min each and contained 65–365 trials.

Tetrodes consisted of four twisted polyimide-coated nichrome wires (H.P.

Reid, single-wire diameter of 12.5 mm, gold plated to 0.2–0.3-MO impedance).

Signals were amplified (20� gain), impedance was buffered using a 16-channel

headstage amplifier (Plexon Instruments HST/8o50-G20), transmitted to a

second amplifier and bandpass filtered (Plexon Instruments PBX2/16sp-G50,

50� gain, 0.3–8-kHz bandpass), and the signals were digitized at 32 kHz

(National Instruments PCI 6259). Spike and whisker data acquisition were

carried out using custom routines in Labview (National Instruments). Electro-

des were advanced by 50–200 microns every 1–2 recording days. Recordings

remained stable at each site, judging by waveforms of isolated units (Fig. 2). At

the completion of all recordings, 3–4 electrolytic lesions were left along the

electrode track. Lesions were recovered in cytochrome oxidase–stained, 100-mm

sections cut 451 coronal from the midsagittal plane, which contain one barrel

column from each of the five whisker rows49. Recording sites were located in

cortical layers 4 and 5, as determined by recording depth27.

Spike data were acquired continuously and single units were isolated offline

using a semi-automated spike- sorting algorithm26 implemented in Matlab

(Mathworks) by S. Mehta (University of California San Diego) and S.P.J.

Briefly, all spike waveforms that surpassed a threshold amplitude of 5–10 s.d.

above noise on any tetrode channel were sampled for sorting. Each waveform

contained 1 ms of data (32 sample points at 32 kHz) for each tetrode channel.

Waveforms were first de-jittered, over-clustered using hierarchical clustering

and aggregated into statistically distinct clusters on the basis of energy and ISI

criteria. Next, clusters were manually evaluated in multiple dimensions

(amplitude, principal components and energy) using a custom graphical user

interface, and re-clustering was performed as necessary. Cluster quality was

evaluated using an ISI criterion (o0.5% violations of a 2-ms refractory period)

and isolation distance50 (isolation distance 420 in eight dimensions). Fast-

spiking and regular-spiking neurons could not be distinguished on the basis of

spike waveforms, so data were pooled across all cells. It is probable that a large

majority of neurons were regular-spiking neurons.

Data analysis. Data analysis was performed using Matlab. Significance was

assessed using Student’s t tests, nonparametric Kolmogorov-Smirnoff tests and

Kruskal-Wallis nonparametric one-way ANOVAs.

Slips were identified as rapid acceleration transients that positively or

negatively surpassed defined acceleration thresholds (Y). A 2-ms interval was

required between subsequent acceleration transients. First slips were defined as

acceleration transients with no preceding threshold crossings for Z20 ms.

Rings were identified as acceleration transients 2–20 ms after a first slip. Slip

velocity was determined as the peak velocity in a 10-ms window centered on

the slip. Thresholds were chosen from the measured s.d. of acceleration on

sandpaper surfaces (1 s.d. ¼ 0.08 mm ms–2). A threshold of ±0.32 mm ms–2

(±4 s.d. above mean acceleration on surfaces) was used for most analyses unless

otherwise noted. A lower threshold (±0.16 mm ms–2) was used to quantify the

proportion of surface-driven spikes attributable to slips (Fig. 4e,f) to include

spikes driven by even the weakest slips. A threshold of 0.16 mm ms–2

corresponded to the weakest slips evoked on surfaces relative to air (Fig. 1

and Supplementary Fig. 1) and matched the average response threshold of S1

neurons (Fig. 6b). Y ¼ ±0.16 mm ms–2 was also used for the analysis of

ringing motion in Supplementary Figure 3.

Slip-aligned PSTHs (2-ms bins) were fit with a BARS algorithm28. Con-

fidence intervals, response latency and jitter were determined from the fits. SRI

was defined as

100� ðpost-slip peak firing rate � pre-slip baseline firing rateÞðpre-slip baseline firing rateÞ

with firing rates being determined from the fits of slip-aligned PSTHs. The

effect of ringing motion on slip response was assessed by generating slip-

response PSTHs separately for first slips without subsequent rings versus first

slips followed by one or more rings. Whisking onset was defined as the time of

first appearance of the whisker in the CCD image. For PSTHs aligned to

whisking onset, only trials that lasted longer than the illustrated time axis were

included. Neurons were classified as being surface responsive, surface inhibited

or nonresponsive (Fig. 4b,d and Supplementary Fig. 4) by testing for

significantly different firing rates between pre-whisk baseline period (100 ms)

and surface whisking period (125–651 ms, mean ¼ 340 ± 42 ms, each trial

terminated by nose poke withdrawal or whisker departure from the CCD

image) across all trials (t test, P o 0.05 criterion).

For the regression analysis, 40-ms cascading windows with 20-ms overlap

were used and only trials longer than 250 ms were considered. Six neurons were

discarded because of a lack of sufficient trials. This analysis used all slips

exceeding Y ¼ ±0.16 mm ms–2. To determine the proportion of surface-driven

spikes attributable to first slips (Fig. 4f), we used a process analogous to boot-

strapping, in which the number of spikes in 20-ms windows following all first

slips was compared with the number of spikes in an equal number of 20-ms

windows whose onset was chosen randomly, but which did not overlap with

slip-response windows (Supplementary Fig. 5). Only the first 250 ms of trials

with total length 4250 ms were used to avoid trivial correlations between spike

counts arising from variable trial length. The number of spikes in non-slip

epochs correlated with the total number of spikes in a trial, as both varied with

neuronal firing rate (Fig. 4f).

Responses to slips of varying accelerations (Fig. 6a) were assessed by

generating PSTHs aligned to all first slips surpassing increasing acceleration

thresholds, starting with low values (Y ¼ ± 0.064 mm ms–2, below 1 s.d. of

acceleration) and increasing to the highest value of Y ¼ ±0.8 mm ms–2 (10 s.d.

of acceleration). The response threshold of a neuron was defined as the lowest

slip acceleration at which a significant peak response was observed and gain was

defined as the change in peak firing probability per 2 s.d. change in accelera-

tion. To assess selectivity for direction of motion, we divided slips in defined

acceleration ranges according to protraction and retraction phases of whisking.

doi:10.1038/nn.2328 NATURE NEUROSCIENCE

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A wide range was used to prevent artifacts resulting from a low number of

spikes, but the mean acceleration of slips in the two directions were highly

similar (difference between mean accelerations in the two directions o0.1 s.d.

of acceleration on P150 surface, for all recording sites; Supplementary Fig. 6).

The modulation index for direction selectivity was defined as

protraction response � rectraction response

average response in both directions

which can vary from 0 (no direction selectivity) to ±2 (selective responses to

only one direction). The selectivity of slip responses to the absolute position of

whisker (determined from position on the CCD array) was assessed by dividing

slips into four interquartile ranges of absolute position (calculated from all

trials at recording site) and testing for significant differences in slip-response

probability (non-parametric one-way ANOVA).

Simulation. Simulation was performed using a Monte Carlo procedure that

numerically implemented measured spike probabilities. Population size was

varied by down-sampling or over-sampling (with replacement) the 90 recorded

neuron profiles. Spike probabilities were obtained from pre- and post-slip

windows in the slip-locked PSTHs for the corresponding slip acceleration.

Quantification of synchronous spiking. To minimize artifacts resulting

from under-sampling, we restricted analysis of synchronous spiking to

slip-responsive neuron pairs that had Z30 synchronous spikes in the 20-

ms response window after slips (n ¼ 63 pairs, median 77 synchronous

spikes, median 845 total sampled slips). Firing synchrony on surfaces was

calculated in cascading 20-ms time windows (10-ms overlap between

windows). We did not detect any significant differences in firing synchrony

or surface encoding between L4 and L5 neurons and pooled the data. The

signal-to-noise ratio for mean firing rate or synchrony (Fig. 8b) was

defined as the ratio of average firing rate or synchrony during surface

whisking (200 ms) to pre-whisk baseline (100 ms). For comparison of

firing synchrony and rate across textures (Fig. 8c–h), these quantities

were measured across all whisking trials. Slip distributions across texture

were compared by two-way ANOVA (Fig. 8c,f). The nonparametric

Kolmogorov-Smirnoff test was used to evaluate firing rate and synchro-

nous spiking rate differences across texture as a result of the low number of

neurons and pairs (Fig. 8d,e,g,h).

48. Venkatachalam, S., Fee, M.S. & Kleinfeld, D. Ultra-miniature headstage with 6-channeldrive and vacuum-assisted micro-wire implantation for chronic recording from theneocortex. J. Neurosci. Methods 90, 37–46 (1999).

49. Allen, C.B., Celikel, T. & Feldman, D.E. Long-term depression induced by sensorydeprivation during cortical map plasticity in vivo. Nat. Neurosci. 6, 291–299(2003).

50. Harris, K.D. et al. Temporal interaction between single spikes and complex spike burstsin hippocampal pyramidal cells. Neuron 32, 141–149 (2001).

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sparse temporal coding of elementary tactile features during active whisker sensation

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