Prediction of primary somatosensory neuron activity during active tactile exploration
A major challenge of sensory neuroscience is to understand the encoding properties of neurons to the point that their spiking activity can be predicted in the awake animal , during natural behaviour . However , accurate prediction is difficult without experimental control of stimulus parameters and , despite early studies of awake , behaving animals ( Hubel , 1959 ) , subsequent work has most often effected experimental control by employing anaesthesia and/or passive stimulation . However , the active character of sensation ( Gibson , 1962; Yarbus , 1967 ) , based on motor control of the sense organs , is lost in reduced preparations . Recent methodological advances permit a way forward: in the whisker system , it is now possible to record neuronal activity from an awake mouse , actively exploring the environment with its whiskers , whilst simultaneously measuring the fundamental sensory variables ( whisker kinematics and mechanics ) likely to influence neuronal activity ( O'Connor et al . , 2010b ) . Our aim here was to predict spikes fired by primary whisker neurons ( PWNs ) of awake mice engaged in natural , object exploration behaviour . The manner in which primary neurons encode sensory information fundamentally constrains all downstream neural processing ( Lettvin et al . , 1959 ) . PWNs innervate mechanoreceptors located in the whisker follicles ( Zucker and Welker , 1969; Rice et al . , 1986 ) . They are both functionally and morphologically diverse; including types responsive to whisker-object contact and/or whisker self-motion ( Szwed et al . , 2003; Ebara et al . , 2002 ) . PWNs project to the cerebral cortex , analogously to other modalities , via trisynaptic pathways through the brainstem and thalamus ( Diamond et al . , 2008 ) . Here , we show that PWN responses are well-predicted by rotational force ( 'moment' ) acting on the whisker , while whisker angle is a poor predictor . Moment coding accounts for spiking during both whisker-object interaction and whisker motion in air . Moment coding can also account for findings in previous studies of passive stimulation in the anaesthetized animal; indicating that the same biomechanical framework can account for primary somatosensory neuron responses across diverse states . Our results provide a mechanical basis for linking receptor mechanisms to tactile behaviour .
We recorded the activity of single PWNs from awake mice ( Figure 1A , E , Figure 1—figure supplement 1 ) as they actively explored a metal pole with their whiskers ( N = 20 units ) . At the same time , we recorded whisker motion and whisker shape using high-speed videography ( 1000 frames/s; Figure 1D , Video 1 ) . As detailed below , PWNs were diverse , with some responding only to touch , others also to whisker motion . Since each PWN innervates a single whisker follicle , we tracked the ‘principal whisker’ of each recorded unit from frame to frame , and extracted both the angle and curvature of the principal whisker in each video frame ( total 1 , 496 , 033 frames; Figure 1B–E; Bale et al . , 2015 ) . Whiskers are intrinsically curved , and the bending moment on a whisker is proportional to how much this curvature changes due to object contact ( Birdwell et al . , 2007 ) : we therefore used ‘curvature change’ as a proxy for bending moment ( O'Connor et al . , 2010a ) . Whisker-pole contacts caused substantial whisker bending ( curvature change ) , partially correlated with the whisker angle ( Figures 1E , 4E ) and , consistent with Szwed et al . ( 2003 ) and Leiser and Moxon ( 2007 ) , robust spiking ( Figures 1E , 2E ) . 10 . 7554/eLife . 10696 . 003Figure 1 . Electrophysiological recording from single primary whisker units in awake , head-fixed mice and simultaneous measurement of whisker kinematics/mechanics . ( A ) Schematic of the preparation , showing a tungsten microelectrode array implanted into the trigeminal ganglion of a head-fixed mouse , whilst a metal pole is presented in one of a range of locations ( arrows ) . Before the start of each trial , the pole was moved to a randomly selected , rostro-caudal location . During this time , the whiskers were out of range of the pole . At the start of the trial , the pole was rapidly raised into the whisker field , leading to whisker-pole touch . Whisker movement and whisker-pole interactions were filmed with a high-speed camera . ( B , C ) Kinematic ( whisker angle θ ) and mechanical ( whisker curvature κ , moment M→ , axial force F→ax and lateral force F→lat ) variables were measured for the principal whisker in each video frame . When a whisker pushes against an object during protraction ( as in panel D , red and cyan frames ) , curvature increases; when it pushes against an object during retraction ( as in panels B and C ) , it decreases . ( D ) Individual video frames during free whisking ( yellow and green ) and whisker-pole touch ( red and cyan ) with tracker solutions for the target whisker ( the principal whisker for the recorded unit , panel E ) superimposed ( coloured curve segments ) . ( E ) Time series of whisker angle , push angle and curvature change , together with simultaneously recorded spikes ( black dots ) and periods of whisker-pole contact ( red bars ) . Coloured dots indicate times of correspondingly coloured frames in D . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00310 . 7554/eLife . 10696 . 004Figure 1—figure supplement 1 . Electrophysiological recording from trigeminal primary neurons of awake , head-fixed mice . Extracellular potential recorded from the same single unit during both anaesthetized and awake epochs . Spikes belonging to the cluster of the target unit are shown by black triangles . Inset shows overlay of all waveforms belonging to this cluster . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00410 . 7554/eLife . 10696 . 005Figure 1—figure supplement 2 . Computation of axial and lateral contact forces . Axial ( Fax ) and lateral ( Flat ) force components at the whisker base were calculated , in each video frame where there were whisker-pole contacts , as follows ( Pammer et al . , 2013 ) . First , the point of whisker-pole contact was located ( Materials and methods ) . The direction of the force F→ was then calculated as the normal to the whisker tangent at the contact point ( Pammer et al . , 2013 ) . Moment at the base M was calculated from the whisker curvature at the base ( Materials and methods ) and then the magnitude F of F→ was derived from the definition of moment:F=Mrsin ( φ ) where r is the magnitude of the lever arm vector r→ from whisker base to contact point , and φ is the angle between r→ and F→ . The components Fax and Flat were then found by projecting F→ onto the tangent and normal to the whisker at its base , respectively:Fax=Fsin ( θbase−θcontact ) , Flat=Fcos ( θbase−θcontact ) . Here θbase is the angle between the tangent to the whisker at its base and the horizontal; θcontact is the angle between F→ and the horizontal . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00510 . 7554/eLife . 10696 . 006Video 1 . Video of an awake mouse , exploring a pole with its whiskers with simultaneous electrophysiological recording of a primary whisker neuron . At the start of the video , the pole is out of range of the whiskers . The whisker tracker solution for the principal whisker of the recorded unit is overlaid in red . White dots represent spikes; orange trace shows whisker angle ( scale bar = 40° ) ; blue trace shows whisker curvature change ( scale bar = 0 . 05 mm-1 ) . Video was captured at 1000 frames/s and is played back at 50 frames/s . Related to Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00610 . 7554/eLife . 10696 . 007Figure 2 . Primary whisker neurons encode whisker curvature , not whisker angle , during active sensation . ( A ) Schematic of the Generalized Linear Model ( GLM ) . ( B ) For an example unit , whisker angle ( top panel ) , whisker curvature change ( middle panel ) and simultaneously recorded spike train ( bottom panel , black ) , together with predicted spike trains for the best-fitting angle GLM ( bottom panel , orange ) and curvature change GLM ( bottom panel , blue ) . Spike trains discretized using 1-ms bins and smoothed with a 100 ms boxcar filter . Prediction performance ( Pearson correlation coefficient , PCC ) for this unit was 0 . 59 . Inset shows tuning curves for both GLMs , computed by convolving the relevant sensory time series ( angle or curvature change ) with the corresponding GLM stimulus filter to produce a time series of filter coefficients , and estimating the spiking probability as a function of filter coefficient ( 25 bins ) . ( C ) Analogous to panel B , for a second example unit . Prediction performance PCC for this unit was 0 . 74 . ( D ) Prediction performance ( PCC between predicted and recorded spike trains ) compared for GLMs fitted with three different types of input: curvature change alone; angle alone; both curvature change and angle . Each blue/orange/green dot is the corresponding PCC for one unit: large black dots indicate median; error bars denote inter-quartile range ( IQR ) . To test statistical significance of each unit’s PCC , the GLM fitting procedure was repeated 10 times on spike trains subjected each time to a random time shift: magenta dots show these chance PCCs for the unit indicated by the magenta circle; the mean chance PCC was computed for each unit and the large grey dot shows the median across units . Black circles indicate units whose PCC was significantly different to chance ( signed-rank test , Bonferroni-corrected , p<0 . 0025 ) . To facilitate direct comparison between results for curvature change GLM and angle GLM , these are re-plotted in the inset . ( E ) Left . Firing rate during touch episodes compared to that during non-touch episodes for each unit , compared to corresponding predicted firing rates from each unit’s curvature change GLM . Right . Medians across units: error bars denote IQR; * denotes differences significant at p<0 . 05 ( signed-rank test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00710 . 7554/eLife . 10696 . 008Figure 2—figure supplement 1 . Effect on GLM performance of quadratic input terms , simulated repeated trials and minimal stimulus filters . ( A ) Angle GLM prediction performance is robust to addition of quadratic stimulus-dependence . Prediction accuracy ( PCC ) for standard angle GLM ( same data as Figure 2D of main text ) in comparison to quadratic GLM ( see Materials and methods ) . Black dots denote medians , error bars IQR . ( B ) Single-trial GLM prediction accuracy is limited by neuronal response variability . Prediction accuracy ( PCC ) for simulated neurons . Each simulated neuron is the best-fitting GLM , based on instantaneous curvature change , for its corresponding recorded unit ( see Materials and methods ) . Prediction accuracy is quantified both using the single-trial approach of the main text and using a repeated-trial method only possible by virtue of using a simulation . Black dots denote medians , error bars IQR . ( C ) Prediction accuracy of curvature-based GLMs is accounted for by tuning to instantaneous curvature change . A GLM performs a temporal filtering operation on its sensory stimulus input and the sensory feature ( s ) which it encodes is determined by this ‘stimulus filter’ . The stimulus filters can , in principle , be complex , but we found that the ability of a GLM to predict spikes ( lower left ) from curvature change was fully explained by the simple case where the action of the stimulus filter is simply to multiply the sensory input by a gain factor ( median 0 . 55 , IQR 0 . 26–0 . 66; p=0 . 35 signed-rank test ) . Recorded spike train ( upper left ) and curvature-predicted spike trains ( lower left ) both for a ‘curvature history’ GLM with a length 5 stimulus filter , as used in Figure 2D of main text , and for an ‘instantaneous curvature’ GLM with a length 1 stimulus filter . Data for unit 2 of main text Figure 2C . Prediction accuracy of the curvature history GLM compared to that of the instantaneous curvature GLM for every recorded unit ( right ) . ( D ) Tuning curves for curvature change ( blue ) and angle ( orange ) of unit 1 and unit 2 in Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00810 . 7554/eLife . 10696 . 009Figure 2—figure supplement 2 . Moment is near-perfectly correlated with axial/lateral contact force components during pole exploration . ( A ) Two example time series for simultaneously measured whisker angle , bending moment , lateral force and axial force ( see Materials and methods ) . Red bars indicate episodes of whisker-pole contact . ( B ) Joint distribution of bending moment and lateral force ( left ) , compared to that of bending moment and axial force ( right ) , for the same recording shown in A . Moment was highly linearly correlated with lateral force ( median absolute correlation coefficient across units 0 . 995 , IQR 0 . 99–1 . 00 , median R2 of linear fit 0 . 99 , IQR 0 . 97–1 . 00 ) , and highly quadratically correlated with axial force ( median R2 of quadratic fit 0 . 94 , IQR 0 . 85–0 . 98 ) . This indicates that , during our conditions of pole exploration , axial force and lateral force are both redundant with moment . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 00910 . 7554/eLife . 10696 . 010Figure 2—figure supplement 3 . Example filters for curvature-based GLMs . Stimulus filter , history filter and bias term of curvature-based GLMs for two units ( A , B ) , fitted as described in Materials and methods . Both units had negative history filters ( in the 2 ms preceding a spike ) , consistent with refractoriness . The stimulus filter of unit B was negative ( in the 5 ms preceding a spike ) , indicating sensitivity to negative curvature change . The stimulus filter of unit A was biphasic , but with positive integral , indicating sensitivity both to positive curvature change and to positive curvature change derivative . Under our stimulus conditions , dominated by slow ( ~100 ms ) time-scale whisker-pole interactions , the former effect was dominant; derivative-sensitivity had relatively little impact on spike prediction . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 01010 . 7554/eLife . 10696 . 011Figure 3 . Primary whisker neurons encode whisker angular acceleration during free whisking . ( A ) Mean response of an example whisking-sensitive unit to whisking amplitude , computed during non-contact episodes ( dark green , shaded area shows SEM ) with regression line ( black ) . Inset shows regression line slopes ( median and IQR ) for whisking sensitive ( green ) and whisking insensitive ( grey ) units . * indicates statistically significant rank-sum test ( p=0 . 05 ) . ( B ) Mean response of two example units as a function of angular acceleration . The dark brown unit is the same as that shown in A . ( C ) Mean response of two example units as a function of whisking phase . The dark pink unit is the same as that reported in A; the light pink unit is the same as that shown as light brown in B . ( D ) Excerpt of free whisking ( orange ) along with activity of an example , whisking-sensitive unit ( black ) and activity predicted by a GLM driven by angular acceleration ( brown ) . The unit is the same as that shown in A . ( E ) GLM prediction accuracy ( PCC ) for all whisking sensitive ( brown ) and whisking insensitive units ( grey ) . Bars and vertical lines denote median and IQR respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 01110 . 7554/eLife . 10696 . 012Figure 3—figure supplement 1 . Whisking-sensitive units exhibit heterogeneous selectivity to angular acceleration . For each whisker-sensitive unit , an acceleration tuning curve was estimated ( Figure 3B ) . Tuning to positive ( negative ) acceleration was quantified by the slope of a regression line fitted to the positive ( negative ) half of the acceleration tuning curve . In general , units responded to both positive and negative accelerations , but to different degrees . Statistical tests , based on regression coefficients , detailed in Materials and methods , were used to differentiate the different types of unit . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 01210 . 7554/eLife . 10696 . 013Figure 4 . Whisker angle and whisker curvature change are highly correlated during passive whisker deflection , but decoupled during active touch . ( A ) Whisker angle ( top ) and whisker curvature change ( bottom ) time series , due to passive , trapezoidal stimulation of C2 whisker in an anaesthetized mouse , estimated as mean over 10 repetitions . Note that error bars ( showing SEM ) are present but very small . ( B ) Corresponding data for low-pass filtered white noise ( hereafter abbreviated to ‘white noise’ ) stimulation of the same whisker . ( C ) Cross-correlation between curvature change and angle during white noise stimulation , for C2 whisker . ( D ) Cross-correlation between angle and curvature change at zero lag , for both passive stimulation under anaesthesia and awake , active sensing ( median of absolute cross-correlation for each unit; error bar denotes IQR ) . ( E ) Joint distribution of whisker angle and whisker curvature change in awake , behaving mice ( 1 ms sampling ) . Different colours denote data corresponding to different recorded units . Inset: Analogous plot for passive , white noise whisker deflection in an anaesthetised mouse . Different colours indicate data from different whiskers . ( F ) Joint distribution of angle and curvature change for an example recording from an awake behaving mouse , with samples registered during touch and non-touch distinguished by colour ( 1 ms sampling ) . ( G ) Touch data of F classified according to pole position ( dot colour ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 01310 . 7554/eLife . 10696 . 014Figure 4—figure supplement 1 . Correlations between angle and curvature change during passive whisker stimulation can make curvature-tuned units appear angle-tuned . The data of Figure 4 show a strong correlation between whisker angle and whisker curvature during passive stimulation of the whisker . To test whether this correlation might make curvature-tuned units appear angle-tuned , we used a simulation approach . This allowed us to generate responses from idealised neurons whose true tuning was known , by construction , to be only to curvature . We simulated responses of such neurons to the curvature change time series obtained from passive white noise stimulation ( A1-2 ) . We then trained a GLM to predict these curvature-evoked spikes using only whisker angle as input ( A3-A4 ) . Despite being fed the ‘wrong’ input , this GLM was able to predict the spikes accurately ( for C2 whisker , angle PCC was 0 . 90 , curvature change PCC 0 . 94; results similar for C5; C ) . This result was robust to different choices of feature tuning ( B-C ) . ( A1 ) Whisker curvature change caused by the white noise stimulus applied to C2 whisker of an anaesthetized mouse ( same data as main text Figure 4B , repeated for clarity ) . ( A2 ) Spike train evoked by a simulated curvature-tuned neuron in response to the stimulus in A1 ( a GLM with the position filter shown in left panel of A5 ) . ( A3 ) Whisker angle time series corresponding to panel A1 . ( A4 ) Target response ( black ) compared to predicted response from best-fitting GLMs using either angle ( orange ) or curvature change ( blue ) as input . ( A5 ) Left . Stimulus filter used to generate the spike train of panel A2 . Middle-Right . Best-fitting stimulus filters ( normalised to unit length ) for GLMs trained on the spikes of panel A2 and the angle time series of panel A3 or the curvature change time series of panel A1 respectively . ( B1-5 ) Results analogous to A1-5 for a simulated neuron tuned to curvature velocity . ( C ) Quantification of the GLM predictions shown in panels A4-B4 . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 01410 . 7554/eLife . 10696 . 015Figure 4—figure supplement 2 . Measurement of whisker bending during passive whisker deflection . ( A ) . Four video frames taken during trapezoidal , passive whisker stimulation with whisker tracker solutions overlaid ( coloured lines ) . ( B ) Curvature change ( left ) and corresponding tracker solutions ( right ) during a 45 ms episode . Coloured dots mark the times of the example frames in panel A and shading from blue to aqua indicates curvature change . This whisker had negative intrinsic curvature . As the actuator applied force to the whisker , the whisker straightened up and the curvature increased . DOI: http://dx . doi . org/10 . 7554/eLife . 10696 . 015 To test between candidate encoding variables , our strategy was to determine how accurately it was possible to predict PWN activity from either the angular position or curvature change of each recorded unit’s principal whisker . To predict spikes from whisker state , we used Generalised Linear Models ( GLMs; Figure 2A ) . GLMs , driven by whisker angle , have previously been shown to provide a simple but accurate description of the response of PWNs to passive stimulation ( Bale et al . , 2013 ) and have mathematical properties ideal for robust parameter-fitting ( Truccolo et al . , 2005; Paninski et al . , 2007 ) . For each recorded unit ( median 69 , 672 frames and 550 spikes per unit ) , we computed the GLM parameters that best predicted the unit’s spike train given the whisker angle time series , using half the data as a training set for parameter-fitting ( 8 total fitted parameters - 5 for stimulus filter , 2 for history filter , 1 bias; Figure 2—figure supplement 3 ) . We then assessed prediction performance using the other half of the data as a testing set: we provided the GLM with the whisker angle time series as input and calculated the predicted spike train , evoked in response ( Materials and methods ) . We then compared the recorded spike train to the GLM-predicted one ( Figure 2B–C ) and quantified the similarity between the smoothed spike trains using the Pearson correlation coefficient ( PCC ) . This is a stringent , single-trial measure of model prediction performance ( Figure 2—figure supplement 1B ) . We then repeated this entire procedure for the whisker curvature time series . Although angle GLMs predicted spike trains of a few units moderately well ( 2/20 units had PCC > 0 . 5 ) , they performed poorly for the majority ( median PCC 0 . 06 , IQR 0 . 019–0 . 3; Figure 2B–D , orange ) . This was unlikely to be because of non-linear tuning to whisker angle , since quadratic GLMs fared only marginally better ( median PCC 0 . 097 , IQR 0 . 042–0 . 31; p=0 . 044 , signed-rank test , Figure 2—figure supplement 1A ) . In contrast , we found that , at the population level , the curvature GLMs were substantially more accurate than the angle GLMs ( median PCC 0 . 52 , IQR 0 . 22–0 . 66; p=0 . 0044 , signed-rank test; Figure 2B–D , blue ) with prediction accuracy up to PCC 0 . 88 . Curvature GLMs also predicted spikes during touch episodes significantly more accurately ( median PCC 0 . 57 , IQR 0 . 23–0 . 72 ) than did angle GLMs during non-touch episodes ( median 0 . 06 , IQR 0 . 02–0 . 35; p=0 . 005 , signed-rank test ) . At the level of individual units , 90% had above chance PCC and we termed these ‘curvature-sensitive’ ( Materials and methods ) . Of the curvature-sensitive units , 61% were sensitive to positive curvature change and 39% to negative curvature change ( Materials and methods ) . The result that curvature predicted PWN responses better than angle was robust to the number of fitted parameters: a GLM sensitive to instantaneous curvature ( 4 parameters: 1 stimulus filter parameter , 2 history filter parameters and 1 bias ) exhibited very similar prediction accuracy ( Figure 2—figure supplement 1C ) . The result was also robust to time-scale: prediction accuracy based on curvature was significantly greater than that based on angle for smoothing time-scales in the range 1–100 ms ( signed-rank test , p<0 . 05 , Bonferroni-corrected ) . Although the activity of most units was better predicted by whisker curvature change than by whisker angle , there was significant variability in prediction performance , and there were a few units for which the angle prediction performance was appreciable ( Figure 2D ) . However , we found that this could largely be attributed to redundancy . When a mouse whisks against an object , curvature change and angle fluctuate in concert ( Birdwell et al . , 2007; Bagdasarian et al . , 2013; Pammer et al . , 2013; Figures 1E , 4E and Figure 4F–G ) . When we fitted GLMs using both curvature change and angle as input , these GLMs predicted the spike trains no more accurately ( median PCC 0 . 53 , IQR 0 . 40–0 . 62; p=0 . 067 , signed-rank test; Figure 2D ) than GLMs based on curvature change alone . Moreover , on a unit-by-unit basis , for 65% of units , curvature change GLMs predicted spikes better than angle ( signed-rank test , p<0 . 05 , Bonferroni-corrected ) ; only for 5% of units did angle predict spikes better than curvature change . GLMs based on curvature change also predicted spike trains more accurately than GLMs based on ‘push angle’ – the change in angle as the whisker pushes against an object ( Figure 1E; median PCC 0 . 25 , IQR 0 . 04–0 . 45; p=0 . 006 , signed-rank test ) . Moreover , prediction accuracy of GLMs fitted with both push angle and curvature change ( median PCC 0 . 52 , IQR 0 . 2–0 . 69 ) inputs was no better than that of GLMs fitted with curvature alone ( p=0 . 43 , signed-rank test ) . In principle , neurons might also be sensitive to the axial force component ( parallel to the whisker follicle ) and/or lateral force component ( orthogonal to axial ) associated with whisker-object contact ( Figure 1B–C , Figure 1—figure supplement 2; Solomon and Hartmann , 2006; Pammer et al . , 2013 ) . We restricted our analysis to bending moment since , under our experimental conditions , axial/lateral force components were near-perfectly correlated with bending moment ( Figure 2—figure supplement 2 ) and bending moment is likely to have a major influence on stresses in the follicle ( Pammer et al . , 2013 ) . To further test the curvature-encoding concept , we asked whether curvature GLMs could account for the response of PWNs to whisker-pole touch . To this end , we parsed the video data into episodes of ‘touch’ and ‘non-touch’ . Units fired at a higher rate during touch than otherwise ( Szwed et al . , 2003; Leiser and Moxon , 2007 ) . Without any further parameter-adjustment , the curvature-based GLMs reproduced this effect ( Figure 2E ) : the correlation coefficient between recorded and GLM-predicted firing rate for touch episodes was 0 . 97 . Collectively , the above results indicate that , during active touch , the best predictor of whisker primary afferent firing is not whisker angle but rather the bending moment . During free whisking - in the absence of whisker-pole contact - whisker curvature , and therefore bending moment , changed little ( Figure 1E , Figure 4F ) ; consistent with previous studies ( Knutsen et al . , 2008; Quist et al . , 2014 ) . Yet , 50% of recorded units ( ‘whisking-sensitive units’ ) were significantly modulated by whisking amplitude ( Figure 3A ) . Consistent with Szwed et al . ( 2003 ) , PWNs were diverse: 45% were curvature-sensitive ( significant PCC for curvature based GLM ) but not whisking-sensitive; 45% were both curvature- and whisking-sensitive; 5% were whisking-sensitive but not curvature-sensitive . The presence of whisking sensitivity suggests that moment due to whisker bending is not the only force that influences PWN activity . A likely candidate is the moment associated with the rotational acceleration of a whisker: this moment is proportional to the whisker’s angular acceleration ( Quist et al . , 2014; Materials and methods ) . Consistent with this possibility , we found that whisking-sensitive units were tuned to angular acceleration ( Figure 3B ) and that 50% of these were phase-modulated ( Figure 3C ) . Angular acceleration tuning was diverse: some units fired to acceleration in a particular direction ( rostral or caudal ) , whilst others responded to acceleration in both directions ( Figure 3B , Figure 3—figure supplement 1 ) . Moreover , for whisking-sensitive units ( but not whisking-insensitive ones ) , quadratic GLMs trained on data from non-touch episodes were able to predict spikes using whisker angle acceleration as input ( Figure 3D–E; whisking-sensitive units , median PCC 0 . 37 , IQR 0 . 18–0 . 58; whisking-insensitive , median PCC -0 . 0071 , IQR -0 . 035–0 . 041; p=0 . 0017 rank-sum test for whisking-sensitive vs whisking-insensitive units ) . For 70% of whisking-sensitive units , directional selectivity for acceleration was consistent with that for curvature . These findings indicate that , in the absence of whisker-object contact , responses of PWNs to whisking itself can be accounted for by sensitivity to the moment associated with angular whisker acceleration . We found , during active object exploration , that curvature change , but not whisker angle , predicts PWN firing . In apparent contrast , studies using passive whisker stimulation have reported that PWNs encode whisker angle and its temporal derivatives ( Zucker and Welker , 1969; Gibson and Welker , 1983; Lichtenstein et al . , 1990; Jones et al . , 2004; Arabzadeh et al . , 2005; Bale and Petersen , 2009; Lottem and Azouz , 2011; Bale et al . , 2013 ) . We wondered whether the discrepancy might be due to differences in whisker mechanics between passive and active stimulation conditions . To test this , we analysed the relationship between angle and curvature change during active touch and compared it to that during passive whisker stimulation . During active pole exploration , angle and curvature change were , over all , only loosely related ( median correlation coefficient 0 . 20 , IQR 0 . 079–0 . 39 , Figures 4D–E ) . Important contributory factors were that the angle-curvature relationship was both different for touch compared to non-touch ( Figure 4F ) and dependent on object location ( Figure 4G ) . In contrast , during passive stimulation , whisker angle was near perfectly correlated with curvature change ( for C2 , correlation coefficients 0 . 96 and 0 . 94 respectively; similar results for C5; Figures 4C–D , Figure 4E , inset and Figure 4—figure supplement 2 ) ; consistent with properties of cantilevered beams ( Birdwell et al . , 2007 ) . Simulations confirmed that , due to the tight relationship between the variables , a unit tuned purely to curvature change can appear tightly tuned to angle ( Figure 4—figure supplement 1 ) . The implication is that apparent sensitivity to whisker angle under passive stimulation conditions can be accounted for by moment-tuning .
In the endeavour to understand how neurons encode and process sensory information , there is a basic tension between the desire for tight experimental control and the desire to study animals under natural , unconstrained conditions . Theories of sensory encoding suggest that neural circuits have evolved to operate efficiently under natural conditions ( Simoncelli and Olshausen , 2001; Reinagel , 2001 ) . Previous studies have succeeded in predicting/decoding spikes evoked by passive presentation of natural sensory stimuli to anaesthetised/immobilised animals ( Lewen et al . , 2001; Arabzadeh et al . , 2005; Pillow et al . , 2008; Mante et al . , 2008; Lottem and Azouz , 2011; Bale et al . , 2013 ) , but it has been difficult to extend this approach to encompass natural , active movement of the sense organs . Here , we have addressed this general issue , taking advantage of experimental possibilities recently created in the whisker system ( O'Connor et al . , 2010a ) , and the ability of computational methods , such as GLMs , to uncover stimulus-response relationships even from data with complex statistical structure ( Paninski et al . , 2007; Fairhall and Sompolinsky , 2014 ) . Our main finding was that responses of PWNs , recorded as an awake mouse actively explores an object with its whiskers , can be predicted from the forces acting on the whiskers . Given that , for each unit , we were attempting to predict the entire ~70 s time course of activity , the variability of the behaviour of untrained mice ( O'Connor et al . , 2010a ) , and the lack of trial-averaging as a noise reduction strategy , it is remarkable that we found model prediction correlation coefficients up to 0 . 88 . A challenge of studying neural coding under unconstrained , awake conditions is that sensory variables tend to correlate . A valuable feature of the GLM training procedure is that it takes such correlations into account . We found that , although whisker angle predicted spikes for a subset of units , this effect was very largely explained by a curvature-coding model , together with the correlation between angle and curvature . Pushing a whisker against an object triggers spiking in many PWNs ( Szwed et al . , 2003; Szwed et al . , 2006; Leiser and Moxon , 2007 ) . Biomechanical modelling by Hartmann and co-workers accounts for this by a framework where the whisker is idealised as an elastic beam , cantilever-mounted in the skin ( Birdwell et al . , 2007; Quist et al . , 2014 ) . When such a beam pushes against an object , the beam bends , causing reaction forces at its base . Our data are in striking agreement with the general suggestion that mechanoreceptor activity is closely related to such reaction forces . Our results show that curvature change associated with contact-induced whisker bending , and acceleration associated with whisker rotation , predict PWN spiking . Our results also provide a mechanical basis for previous findings: our finding of subtypes of curvature-only and curvature-acceleration PWNs is consistent with previous reports of ‘touch’ and ‘whisking-touch’ units ( Szwed et al . , 2003; 2006 ) . Thus , a common framework accounts for diverse PWN properties . Our finding that whisker angle predicts PWN spikes poorly indicates that whisker angle can change without modulating mechanotransduction in the follicle . This is consistent with evidence that , during artificial whisking , the follicle-shaft complex moves as a rigid unit ( Bagdasarian et al . , 2013 ) . In apparent contrast , previous studies using passive stimulation in anaesthetised animals have consistently reported a tight relationship between whisker kinematics and PWN response . In the cantilever whisker model , passively induced changes in whisker angle correlate highly with whisker bending . We confirmed that this applies to real whiskers in vivo and demonstrate that moment-sensitive units can thereby appear angle-tuned . In this way , moment-encoding can account for primary neuron responses not only during active touch but also under passive stimulation . More generally , our results highlight the importance of studying neurons under natural , active sensing conditions . In this study , we considered PWN encoding under conditions of pole contact , since this is well-suited to reaction force estimation ( O'Connor et al . , 2010a; Pammer et al . , 2013 ) and involves object-stimulus interactions on a ~100 ms time-scale that is conducive to single-trial analysis . Since whisker bending is ubiquitous in whisking behaviour , it is likely that our finding of curvature sensitivity is a general one . However , prediction performance varied across units , suggesting that other force components may also be encoded . Other experimental conditions – for example , textured surfaces – may involve multiple force components ( Quist and Hartmann , 2012; Pammer et al . , 2013; Bagdasarian et al . , 2013 ) and/or encoding of information by spike timing on a finer time-scale ( Panzeri et al . , 2001; Petersen et al . , 2001; Arabzadeh et al . , 2005; Bale et al . , 2015 ) . It is axiomatic that mechanoreceptors are sensors of internal forces acting in the tissue within which they are embedded ( Abraira and Ginty , 2013 ) and therefore valuable to be able to measure mechanical forces in the awake , behaving animal . In general , including the important case of primate hand-use , the complex biomechanics of skin makes force-estimation difficult ( Phillips and Johnson , 1981 ) . In contrast , for whiskers , the quasi-static relationship is relatively simple: the bending moment on a whisker is proportional to its curvature . This has the important implication that reaction forces can be directly estimated from videography in vivo ( Birdwell et al . , 2007; O'Connor et al . , 2010a; Pammer et al . , 2013 ) . Our results are the first direct demonstration that such reaction forces drive primary sensory neuron responses – likely involving Piezo2 ion channels ( Woo et al . , 2014; Poole et al . , 2015; Whiteley et al . , 2015 ) – and provide insight into how sensitivity to touch and self-motion arises in the somatosensory pathway ( Szwed et al . , 2003; Yu et al . , 2006; Curtis and Kleinfeld , 2009; Khatri et al . , 2009; O'Connor et al . , 2010b; Huber et al . , 2012; Petreanu et al . , 2012; Peron et al . , 2015 ) . Extraction of bending moment is a useful first step for many tactile computations . Large transients in bending moment signal object-touch events , and the magnitude of bending is inversely proportional to the radial distance of contact along the whisker ( Solomon and Hartmann , 2006 ) . As illustrated by our results on the statistics of active touch , if integrated with cues for whisker self-motion , whisker bending can be a cue to the 3D location of an object ( Szwed et al . , 2003; 2006; Birdwell et al . , 2007; Bagdasarian et al . , 2013; Pammer et al . , 2013 ) . Bending moment can permit wall following ( Sofroniew et al . , 2014 ) and , if integrated across whiskers , can in principle be used both to infer object shape ( Solomon and Hartmann , 2006 ) and to map the spatial structure of the environment ( Fox et al . , 2012; Pearson et al . , 2013 ) . We have shown that the responses of primary whisker neurons can be predicted , during natural behaviour that includes active motor control of the sense organ , from forces acting on the whiskers . These results provide a bridge linking receptor mechanisms to behaviour .
Mice ( C57; N=10; 6 weeks at time of implant ) were anesthetized with isoflurane ( 2% by volume in O2 ) , mounted in a stereotaxic apparatus ( Narishige , London , UK ) and body temperature maintained at 37°C using a homeothermic heating system . The skull was exposed and a titanium head-bar ( 19 . 1 × 3 . 2 × 1 . 3 mm; O'Connor et al . , 2010a ) was first attached to the skull ~1 mm posterior to lambda ( Vetbond , St . Paul , MN ) , and then fixed in place with dental acrylic ( Lang Dental , Wheeling , IL ) . A craniotomy was made ( +0 . 5 to -1 . 5 mm posterior to bregma , 0-3 mm lateral ) and sealed with silicone elastomer . Buprenorphine ( 0 . 1 mg/kg ) was injected subcutaneously for postoperative analgesia and the mouse left to recover for at least 5 days . Mice were studied in a pole exploration apparatus adapted from O'Connor et al . , 2010a , but were not trained on any task . A mouse was placed inside a perspex tube ( inner diameter 32 mm ) , from which its head emerged at one end , and immobilised by fixing the head-bar to a custom mount holder . The whiskers were free of the tube at all times . The stimulus object was a 1 . 59 mm diameter metal pole , located ~3 . 5 mm lateral to the mouse’s snout . To allow control of its anterior/posterior location , the pole was mounted on a frictionless linear slide ( NDN 2-50 . 40 , Schneeberger , Roggwil , Germany ) and coupled to a linear stepper motor ( NA08B30 , Zaber , Vancouver , Canada ) . To allow vertical movement of the pole into and out of range of the whiskers , the pole/actuator assembly was mounted on a pneumatic linear slide ( SLS-10-30-P-A , Festo , Northampton , UK ) , powered by compressed air . The airflow was controlled by a relay ( Weidmüller , Richmond , VA ) . In this way , the pole moved rapidly ( ~0 . 15 s ) into and out of range of the whiskers . The apparatus was controlled from Matlab via a real-time processor ( RX8 , TDT , Alachua , FL ) . We recorded the activity of PWNs from awake mice in the following way . To permit reliable whisker tracking ( see below ) , before each recording session , A , B and E whisker rows were trimmed to the level of the fur , under brief isoflurane anaesthesia . The trigeminal ganglion was targeted as previously described ( Bale et al . , 2015 ) . The silicone seal was removed and a 3/4 shank tungsten microelectrode array ( FHC , Bowdoin , ME , recording electrodes 8 MΩ at 1 kHz , reference 1 MΩ; tip spacing ~500 μm ) was lowered through the brain ( angle 4° to vertical in the coronal plane ) using a micromanipulator ( PatchStar , Scientifica , Uckfield , UK ) under isoflurane anaesthesia . Extracellular potentials were pre-amplified , digitised ( 24 . 4 kHz ) , filtered ( band pass 300–3000 Hz ) and acquired continuously to hard disk ( RZ5 , TDT ) . The trigeminal ganglion was encountered 6–7 mm vertically below the pial surface and whisker-response units identified by manual deflection of the whiskers with a small probe . Once a well-isolated unit was found , the whisker that it innervated ( the ‘principal whisker’ , PW ) was identified by manual stimulation . To define the PW , we deflected not only untrimmed whiskers , but also the stubs of the trimmed whiskers . Any unit whose PW was a trimmed whisker was ignored . At this point , anaesthesia was discontinued . Once the mouse was awake , we recorded neuronal activity during repeated presentations of the pole ( ‘trials’ ) . Before the start of each trial , the pole was in the down position , out of reach of the whiskers . The pole was first moved anterior-posteriorly to a position chosen randomly out of a set of 11 possible positions , spanning a range ± 6 mm with respect to the resting position of the base of the PW . A trial was initiated by activating the pneumatic slide relay , thus moving the pole up into the whisker field , where it remained for 3 s before being lowered . At the end of a recording session , the microelectrode array was withdrawn , the craniotomy sealed with silicone elastomer , and the mouse returned to its home cage . Using the method of O'Connor et al . ( 2010a ) to image whisker movement/shape , whiskers ipsilateral to the recorded ganglion were illuminated from below using a high-power infrared LED array ( 940 nm; LED 940-66-60 , Roithner , Vienna , Austria ) via a diffuser and condensing lens . The whiskers were imaged through a telecentric lens ( 55-349 , Edmunds Optics , Barrington , NJ ) mounted on a high speed camera ( LTR2 , Mikrotron , Unterschleissheim , Germany; 1000 frames/s , 0 . 4 ms exposure time ) . The field of view of the whiskers was 350×350 pixels , with pixel width 0 . 057 mm . Mouse whisking behaviour during the awake recording was segmented into ‘touch' , and 'non-touch’ episodes . Touches between the PW of each unit and the pole were detected manually in each frame of the high-speed video . A frame was scored as touch if no background pixels were visible between the pole silhouette and the whisker . Any frame not scored as a touch was scored as non-touch . Touch and non-touch firing rates for a given unit were computed by averaging activity over all corresponding episodes . Since the trigeminal ganglion lacks topography , it is difficult to target units that innervate a specific whisker , and therefore desirable for a whisker tracker to be robust to the presence of multiple rows of whiskers . However , since neurons in the ganglion innervate individual whiskers , it is sufficient to track only one whisker ( the PW ) for each recorded neuron . To extract kinematic/mechanical whisker information , we therefore developed a whisker tracker ( ‘WhiskerMan’; Bale et al . , 2015 ) whose design criteria , different to those of other trackers ( Perkon et al . , 2011; Clack et al . , 2012 ) , were to: ( 1 ) be robust to whisker cross-over events; ( 2 ) track a single , target whisker; ( 3 ) track the proximal segment of the whisker shaft . The shape of the target whisker segment was described by a quadratic Bezier curve r ( t , s ) ( a good approximation away from the zone of whisker-object contact; Quist and Hartmann , 2012; Pammer et al . , 2013 ) : r ( t , s ) = [x ( t , s ) , y ( t , s ) ] , where x , y are horizontal/vertical coordinates of the image , s = [0 , . . , 1] parameterises ( x , y ) location along the curve and t is time . We fitted such a Bezier curve to the target whisker in each image frame using a local , gradient-based search . The initial conditions for the search were determined by extrapolating the solution curves from the previous two frames , assuming locally constant , angular velocity . The combination of the low-parameter whisker description and the targeted , local search made the algorithm robust to whisker cross-over events . The ‘base’ of the target whisker was defined as the intersection between the extrapolated Bezier curve and the snout contour ( estimated as described in Bale et al . , 2015 ) . The solution curve in each frame was visually checked and the curves manually adjusted to correct occasional errors . The whisker angle ( θ ) in each frame was measured as the angle between the tangent to the whisker curve at the base and the anterior-posterior axis ( Figure 1C ) . Whisker curvature ( κ ) was measured at the base as κ=x'y''−x''y' ( x'2+y'2 ) 3/2 , where x' , y' and x'' , y'' are the first and second derivatives of the functions x ( s ) and y ( s ) with respect to s ( Figure 1C ) . Since reaction force at the whisker base reflects changes in whisker curvature , rather than the intrinsic ( unforced ) curvature ( Birdwell et al . , 2007 ) , we computed ‘curvature change’ Δκ = κ - κint , where κint , the intrinsic curvature , was estimated as the average of κ in the first 100 ms of the trial ( before pole contact; O'Connor et al . , 2010a ) . During free whisking , whisker angle oscillated with the characteristic whisking rhythm , but curvature changed little . The small changes in whisker curvature during free whisking were consistent with torsional effects ( Knutsen et al . , 2008 ) . We estimated the number of whisking cycles from the duration of touch/non-touch episodes and the whisking frequency: median 419 whisking cycles per unit during touch periods; 415 during non-touch periods . Under conditions of whisking against a smooth surface , such as in the present study , the quasi-static framework of Birdwell et al . ( 2007 ) applies . Δκ , measured , at the base of a whisker , in the horizontal plane , is proportional to the component of bending moment in that plane . We used Δκ as a proxy for bending moment . Bending moment ( M ) , Axial ( F→ax ) and lateral force ( F→lat ) at the whisker base were calculated , during periods of whisker-pole contact , using the method of Pammer et al . ( 2013 ) , using published data on areal moment of inertia of mouse whiskers ( Pammer et al . , 2013 ) , along with whisker-pole contact location ( see Figure 1—figure supplement 2 for details ) . Pole location , in the horizontal plane , in each frame , was identified as the peak of a 2D convolution between the video image and a circular pole template . To localise whisker-pole contact , the whisker tracker was used to fit the distal segment of the whisker close to the pole , seeded by extrapolation from the whisker tracking solution for the proximal whisker segment , described above . Whisker-pole contact location was defined as the point where this distal curve segment was closest to the detected pole centre . Pole and contact locations were verified by visual inspection . As expressed by Newton’s second law of rotational motion , the moment – or torque – of a rigid body , rotating in a plane , is proportional to the body’s angular acceleration . During free whisking , a whisker behaves approximately as a rigid body and , for the whiskers considered in this study , their motion is predominantly in the horizontal plane ( Bermejo et al . , 2002; Knutsen et al . , 2008 ) . Thus , to assess whether such a moment is encoded by PWNs , we measured angular whisker acceleration during free whisking as a proxy . Acceleration was calculated from the whisker angle time series after smoothing with a Savitzky-Golay filter ( polynomial order 5; frame size 31 ms ) . Push angle – the change in angle as a whisker pushes against an object - was measured during touch epochs . For each touch episode , we determined the value of the angle in the frame before touch onset and subtracted this from the whisker angles during the touch . To determine how whiskers move/bend in response to passive deflection under anaesthesia , a mouse was anesthetized ( isoflurane 2% ) and placed in the head-fixation apparatus . Individual whiskers ( C2 and C5 trimmed to 5 mm ) were mechanically deflected using a piezoelectric actuator as previously described ( Bale et al . , 2013; 2015 ) . All other whiskers were trimmed to the level of the fur . Each whisker , in turn , was inserted into a snugly fitting plastic tube attached to the actuator , such that the whisker entered the tube 2 mm from the face . Two stimuli were generated via a real-time processor ( TDT , RX8 ) : ( 1 ) a 10 Hz trapezoidal wave ( duration 3 s , amplitude 8° ) ; ( 2 ) Gaussian white noise ( duration 3 s , smoothed by convolution with a decaying exponential: time constant 10 ms; amplitude SD 2 . 1° ) . During the stimulation , the whiskers were imaged as detailed above ( 1000 frames/s , 0 . 2 ms exposure time ) .
Primary sensory neurons form the interface between world and brain . Their function is well-understood during passive stimulation but , under natural behaving conditions , sense organs are under active , motor control . In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration , we recorded from primary mechanosensory neurons of awake , head-fixed mice as they explored a pole with their whiskers , and simultaneously measured both whisker motion and forces with high-speed videography . Using Generalised Linear Models , we found that primary neuron responses were poorly predicted by whisker angle , but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion . These results are in apparent contrast to previous studies of passive stimulation , but could be reconciled by differences in the kinematics-force relationship between active and passive conditions . Thus , simple statistical models can predict rich neural activity elicited by natural , exploratory behaviour involving active movement of sense organs .
The brain receives information from the world through the senses . In particular , cells called sensory neurons can detect signals from the environment and relay the information to the brain . A critical test of how well we understand the role of a given sensory neuron is whether it is possible to predict its activity under natural conditions . Previous research has succeeded in predicting the responses of sensory neurons in animals that were anaesthetised . However , it has been difficult to extend this approach to awake animals . Mice and other rodents rely on their whiskers to tell them about their surroundings . Campagner et al . set out to predict how the sensory neurons that send information from whiskers ( or ‘whisker neurons’ ) to the brain would respond in awake mice that were actively exploring an object in their environment . The approach involved using high-speed video ( 1 , 000 frames per second ) to film the whiskers while the mice used them to explore a thin metal pole . At the same time , Campagner et al . recorded the electrical activity of the whisker neurons . The videos were used to calculate the forces acting on the whiskers , and then computational models were used to relate the activity of the neurons to the forces . This approach allowed Campagner et al . to predict the responses of the whisker neurons , even when the mice were exploring the pole freely and unpredictably , simply from knowledge of the forces that were acting on the whiskers . Together , these findings move the field of neuroscience forward by showing that sensory signals and neuronal responses can be correlated even in an awake animal . A key challenge for the future will be to further extend the approach to investigate how the signal conveyed by sensory neurons is transformed by neural circuits within the brain .