Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

On-going computation of whisking phase by mechanoreceptors

Abstract

To attribute spatial meaning to sensory information, the state of the sensory organ must be represented in the nervous system. In the rodent's vibrissal system, the whisking-cycle phase has been identified as a key coordinate, and phase-based representation of touch has been reported in the somatosensory cortex. Where and how phase is extracted in the ascending afferent pathways remains unknown. Using a closed-loop interface in anesthetized rats, we found that whisking phase is already encoded in a frequency- and amplitude-invariant manner by primary vibrissal afferents. We found that, for naturally constrained whisking dynamics, such invariant phase coding could be obtained by tuning each receptor to a restricted kinematic subspace. Invariant phase coding was preserved in the brainstem, where paralemniscal neurons filtered out the slowly evolving offset, whereas lemniscal neurons preserved it. These results demonstrate accurate, perceptually relevant, mechanically based processing at the sensor level.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Closed-loop muscle-computer interface generates naturalistic whisking in anesthetized rats.
Figure 2: Representation of naturalistic whisker motion by follicle mechanoreceptors.
Figure 3: Invariance coding and modeling.
Figure 4: Representation of naturalistic whisker motion in the brainstem.
Figure 5: Early information processing in whisking-related afferent pathways.

References

  1. Ebara, S., Kumamoto, K., Matsuura, T., Mazurkiewicz, J.E. & Rice, F.L. Similarities and differences in the innervation of mystacial vibrissal follicle-sinus complexes in the rat and cat: a confocal microscopic study. J. Comp. Neurol. 449, 103–119 (2002).

    Article  PubMed  Google Scholar 

  2. Maravall, M. & Diamond, M.E. Algorithms of whisker-mediated touch perception. Curr. Opin. Neurobiol. 25, 176–186 (2014).

    CAS  Article  PubMed  Google Scholar 

  3. Kleinfeld, D. & Deschênes, M. Neuronal basis for object location in the vibrissa scanning sensorimotor system. Neuron 72, 455–468 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Crochet, S. & Petersen, C.C. Correlating whisker behavior with membrane potential in barrel cortex of awake mice. Nat. Neurosci. 9, 608–610 (2006).

    CAS  Article  PubMed  Google Scholar 

  5. Arkley, K., Grant, R.A., Mitchinson, B. & Prescott, T.J. Strategy change in vibrissal active sensing during rat locomotion. Curr. Biol. 24, 1507–1512 (2014).

    CAS  Article  PubMed  Google Scholar 

  6. Sofroniew, N.J., Cohen, J.D., Lee, A.K. & Svoboda, K. Natural whisker-guided behavior by head-fixed mice in tactile virtual reality. J. Neurosci. 34, 9537–9550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lenschow, C. & Brecht, M. Barrel cortex membrane potential dynamics in social touch. Neuron 85, 718–725 (2015).

    CAS  Article  PubMed  Google Scholar 

  8. Szwed, M., Bagdasarian, K. & Ahissar, E. Encoding of vibrissal active touch. Neuron 40, 621–630 (2003).

    CAS  Article  PubMed  Google Scholar 

  9. Ahissar, E. & Knutsen, P.M. Object localization with whiskers. Biol. Cybern. 98, 449–458 (2008).

    Article  PubMed  Google Scholar 

  10. Curtis, J.C. & Kleinfeld, D. Phase-to-rate transformations encode touch in cortical eurons of a scanning sensorimotor system. Nat. Neurosci. 12, 492–501 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Tonomura, S. et al. Structure-function correlations of rat trigeminal primary neurons: Emphasis on club-like endings, a vibrissal mechanoreceptor. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci. 91, 560–576 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Wolpert, D.M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998).

    CAS  Article  PubMed  Google Scholar 

  13. Hill, D.N., Curtis, J.C., Moore, J.D. & Kleinfeld, D. Primary motor cortex reports efferent control of vibrissa motion on multiple timescales. Neuron 72, 344–356 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Deutsch, D., Pietr, M., Knutsen, P.M., Ahissar, E. & Schneidman, E. Fast feedback in active sensing: touch-induced changes to whisker-object interaction. PLoS One 7, e44272 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Bale, M.R., Davies, K., Freeman, O.J., Ince, R.A. & Petersen, R.S. Low-dimensional sensory feature representation by trigeminal primary afferents. J. Neurosci. 33, 12003–12012 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Sharpee, T., Rust, N.C. & Bialek, W. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput. 16, 223–250 (2004).

    Article  PubMed  Google Scholar 

  17. Quist, B.W., Seghete, V., Huet, L.A., Murphey, T.D. & Hartmann, M.J. Modeling forces and moments at the base of a rat vibrissa during noncontact whisking and whisking against an object. J. Neurosci. 34, 9828–9844 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Yu, C. et al. Coding of object location in the vibrissal thalamocortical system. Cereb. Cortex 25, 563–577 (2013).

    Article  PubMed  Google Scholar 

  19. Yu, C., Derdikman, D., Haidarliu, S. & Ahissar, E. Parallel thalamic pathways for whisking and touch signals in the rat. PLoS Biol. 4, e124 (2006).

    PubMed  PubMed Central  Google Scholar 

  20. Mohar, B., Katz, Y. & Lampl, I. Opposite adaptive processing of stimulus intensity in two major nuclei of the somatosensory brainstem. J. Neurosci. 33, 15394–15400 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Furuta, T. et al. Inhibitory gating of vibrissal inputs in the brainstem. J. Neurosci. 28, 1789–1797 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Xiang, C., Arends, J.J. & Jacquin, M.F. Whisker-related circuitry in the trigeminal nucleus principalis: ultrastructure. Somatosens. Mot. Res. 31, 141–151 (2014).

    Article  PubMed  Google Scholar 

  23. Knutsen, P.M., Biess, A. & Ahissar, E. Vibrissal kinematics in 3D: tight coupling of azimuth, elevation, and torsion across different whisking modes. Neuron 59, 35–42 (2008).

    CAS  Article  PubMed  Google Scholar 

  24. Maravall, M., Alenda, A., Bale, M.R. & Petersen, R.S. Transformation of adaptation and gain rescaling along the whisker sensory pathway. PLoS One 8, e82418 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Fairhall, A.L., Lewen, G.D., Bialek, W. & de Ruyter Van Steveninck, R.R. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792 (2001).

    CAS  Article  PubMed  Google Scholar 

  26. Seung, H.S. & Sompolinsky, H. Simple models for reading neuronal population codes. Proc. Natl. Acad. Sci. USA 90, 10749–10753 (1993).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Knutsen, P.M., Pietr, M. & Ahissar, E. Haptic object localization in the vibrissal system: behavior and performance. J. Neurosci. 26, 8451–8464 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Saraf-Sinik, I., Assa, E. & Ahissar, E. Motion makes sense: an adaptive motor-sensory strategy underlies the perception of object location in rats. J. Neurosci. 35, 8777–8789 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Sanchez-Jimenez, A., Panetsos, F. & Murciano, A. Early frequency-dependent information processing and cortical control in the whisker pathway of the rat: electrophysiological study of brainstem nuclei principalis and interpolaris. Neuroscience 160, 212–226 (2009).

    CAS  Article  PubMed  Google Scholar 

  30. Sreenivasan, V., Karmakar, K., Rijli, F.M. & Petersen, C.C. Parallel pathways from motor and somatosensory cortex for controlling whisker movements in mice. Eur. J. Neurosci. 41, 354–367 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Timofeeva, E., Dufresne, C., Sík, A., Zhang, Z.W. & Deschênes, M. Cholinergic modulation of vibrissal receptive fields in trigeminal nuclei. J. Neurosci. 25, 9135–9143 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Deschenes, M. & Urbain, N. Vibrissal afferents from trigeminus to cortices. Scholarpedia 4, 7454 (2009).

    Article  Google Scholar 

  33. Veinante, P. & Deschênes, M. Single- and multi-whisker channels in the ascending projections from the principal trigeminal nucleus in the rat. J. Neurosci. 19, 5085–5095 (1999).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Bishop, G.H. The relation between nerve fiber size and sensory modality: phylogenetic implications of the afferent innervation of cortex. J. Nerv. Ment. Dis. 128, 89–114 (1959).

    CAS  Article  PubMed  Google Scholar 

  35. Fee, M.S., Mitra, P.P. & Kleinfeld, D. Central versus peripheral determinants of patterned spike activity in rat vibrissa cortex during whisking. J. Neurophysiol. 78, 1144–1149 (1997).

    CAS  Article  PubMed  Google Scholar 

  36. Derdikman, D. et al. Layer-specific touch-dependent facilitation and depression in the somatosensory cortex during active whisking. J. Neurosci. 26, 9538–9547 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Semba, K. & Egger, M.D. The facial “motor” nerve of the rat: control of vibrissal movement and examination of motor and sensory components. J. Comp. Neurol. 247, 144–158 (1986).

    CAS  Article  PubMed  Google Scholar 

  38. Shoykhet, M., Doherty, D. & Simons, D.J. Coding of deflection velocity and amplitude by whisker primary afferent neurons: implications for higher level processing. Somatosens. Mot. Res. 17, 171–180 (2000).

    CAS  Article  PubMed  Google Scholar 

  39. Paxinos, G. & Watson, C. The Rat Brain in Stereotaxic Coordinates, 4th edn (Academic Press, 1998).

  40. Bagdasarian, K. et al. Pre-neuronal morphological processing of object location by individual whiskers. Nat. Neurosci. 16, 622–631 (2013).

    CAS  Article  PubMed  Google Scholar 

  41. Kawato, M. Internal models for motor control and trajectory planning. Curr. Opin. Neurobiol. 9, 718–727 (1999).

    CAS  Article  PubMed  Google Scholar 

  42. Haidarliu, S. & Ahissar, E. Spatial organization of facial vibrissae and cortical barrels in the guinea pig and golden hamster. J. Comp. Neurol. 385, 515–527 (1997).

    CAS  Article  PubMed  Google Scholar 

  43. Zucker, E. & Welker, W.I. Coding of somatic sensory input by vibrissae neurons in the rat's trigeminal ganglion. Brain Res. 12, 138–156 (1969).

    CAS  Article  PubMed  Google Scholar 

  44. Chichilnisky, E.J. A simple white noise analysis of neuronal light responses. Network 12, 199–213 (2001).

    CAS  Article  PubMed  Google Scholar 

  45. Tsodyks, M.V. & Markram, H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA 94, 719–723 (1997).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank D. Kleinfeld, N. Ulanovsky and A. Finkelstein for discussions. This research was supported by the Israel Science Foundation (grant no. 1127/14), the Minerva Foundation funded by the Federal German Ministry for Education and Research, the United States-Israel Binational Science Foundation (BSF, grant no. 2011432), the NSF-BSF Brain Research EAGER program (grant no. 2014906) and a fund from Lord Alliance for Life Science Collaboration. E.A. holds the Helen Diller Family Professorial Chair of Neurobiology.

Author information

Authors and Affiliations

Authors

Contributions

A.W. and E.A. designed the experiments and the analyses. A.W. and K.B. performed the experiments. A.W. analyzed the data. All of the authors discussed the results and interpretations. A.W. wrote the manuscript. All of the authors discussed and commented on the manuscript.

Corresponding author

Correspondence to Ehud Ahissar.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Dynamics of whisker pad closed-loop control.

(a) Controller scheme. The controller has a two components architecture: (i) a feedforward Inverse Model (IM) which maps the desired local trajectory ( and ) to a motor command . This model was realized by an artificial neural network (with one hidden layer of 3 logistic neurons and a single linear output neuron) trained iteratively at the beginning of each experiment. (ii) a feedback Proportional-Integral-Derivative (PID) controller which uses the error en = - θn-1 to produce a motor correction signal . This signal is required to compensate for inverse model inaccuracy and to track changes in the controlled motor plant, i.e., the whisker pad complex. Note that since stimulation rate is 83Hz and whisker tracking rate is 500Hz, each value of θ * and θ is in fact a vector of 6 numbers. (b) The dynamics of the PID output during synthetic whisking in two different rats. The protocol consisted of 7s long whisking bouts (shaded areas) separated by 5s rest periods. Changes in PID output reflect the dynamics of the motor plant, likely due to muscle fatigue. (c) Short-term dynamics of the change in PID output (mean±std of the last 10 bouts of synthetic whisking portrayed in panel b). In one rat the output transiently increased (purple), signifying muscle ‘depression’, while in the other the output decreased (blue), signifying muscle ‘potentiation’. (d) Long-term dynamics of the PID output (mean±std within each bout in panel b). One rat exhibited drastic potentiation (expressed in a decrease in PID output, purple) followed by long-term stability, while the other exhibited slowly evolving depression (blue). These different behaviors, both in the short- and long- term, probably reflect different ratios of protracting/retracting muscles stimulated in each experiment. The advantage of the PID controller is that it can track these multiple-timescale processes without the need to model them.

Supplementary Figure 2 Synthetic whisking statistics.

(a) Distribution of whisking amplitudes tested (0≤ θ amp ≤18). (b) Distribution of whisking offsets (relative to resting position) tested (0≤ θ off ≤35). (c) Distribution of whisking frequencies tested (4≤f≤10). (d), Distribution of the ratio between protraction and retraction durations. (e), Normalized autocorrelation functions of the four variables in panels a-d. Note that while the amplitude and offset were gradually changed between cycles, both whisking frequency and protraction-retraction ratio were randomized for each cycle independently (hence their autocorrelation drops steeply within the duration of a single cycle).

Supplementary Figure 3 Models of TG neurons.

(a) Example of an LNP model constructed for a TG unit. The whisker trajectory recorded in-vivo is fed into the causal MID filter of the neuron. The output of this filter (i.e., the MID projection of the whisker kinematics) is then transformed into rate using the non-linear conditional probability distribution of the neuron, p(r|θMID). Finally, spike trains are randomly generated from the resulting rate dynamics. (b) Kinematic model constructed for the same unit. The whisker trajectory and its derivative are fed into a bivariate Gaussian fit of the first-order kinematic tuning map. The output is then used to generate Poisson spike trains. (c) Overall first-order kinematic response of the unit (left) and of its models. (d) Phase response of the unit presented in Fig. 3c and of its models, plotted in polar coordinates. Complex summation of the polar histogram yields the ‘phase vector’ of the response pϕ (arrows, see Methods); the argument of this vector is the preferred phase , and its length is the phase selectivity S. Note that the selectivity of both models is smaller than that of the cell (see Figure 3d). (e) Preferred phases of both models match those of the recorded units (R2=0.88 and 0.94 for LNP and kinematic models, respectively).

Supplementary Figure 4 Comparison of response to different stimulation protocols.

(a) Response of exemplary TG cell to synthetic whisking (left), awake playback (middle) and open-loop square wave (right) protocols. Top: cycle kinematics after offset removal and normalization of both amplitude and frequency (bold line is median, shaded area is IQR). Open-loop kinematics, while capturing some basic aspects of whisking, depart from those of natural whisking by having a ‘square wave’ appearance (sharp protraction followed by an epoch of stillness in the fully-protracted state). The kinematic tuning (2nd row), rhythmic tuning (3rd row) and frequency-invariance (bottom row) maps are presented for each protocol. Note the poor coverage of parameter space in the open-loop method. (b) Phase tuning of the cell presented in a, normalized to ease comparison. The phase response is very similar in the two closed-loop protocols but differs for the open-loop case (note the double-peaked curve). (c) The difference in preferred phase between ‘awake-replay’ and synthetic whisking (left) and between open-loop and synthetic whisking (right). The open-loop preferred phase is both more varied (p=0.046, bootstrap, n=22) and biased upward (p=0.006, Wilcoxon signed rank test, n=22). Inset: cumulative distribution functions. (d) Preferred phase of all TG and brainstem units in the awake playback protocol (ordinate) vs. the synthetic whisking protocol (abscissa).

Supplementary Figure 5 Comparison of phase selectivity within restricted kinematic loci.

(a) First-order kinematic tuning of an exemplary TG cell. Four restricted loci are marked and numbered. (b) Phase tuning of the cell (blue) and its kinematic model (yellow) in the marked loci. As the model’s firing rate is derived solely from the first-order kinematics (i.e., the location in the map depicted in (a)), it has no phase selectivity within these restricted regions. In contrast, the cell manifests salient selectivity of phases around its preferred phase (~-0.4π). This indicates that other variables, beyond angle and angular velocity, contribute to the cell’s phase selectivity. (c) To quantify the analysis presented in (b), the conditional information content was calculated for each cell and its model using the formula: (i.e., the average phase information conveyed by the cell/model, given the 1st-order kinematic state). The cells had significantly more conditional phase information than their respective models (p<1e-3, Wilcoxon signed rank test for the difference in information, n=41 phase-informative TG cells and their kinematic models). Inset: cumulative distribution of the difference in information.

Supplementary Figure 6 Histology.

(a) Parasagittal slice of the rat brainstem showing two recording sites in the ventral PrV, marked by lesions (yellow arrowheads). A penetration track is also visible (blue arrows). (b) Parasagittal slice of the rat brainstem showing three recording sites in the rostral part of SpVi (yellow arrowheads). SpVo: oral subdivision of the spinal trigeminal nucleus; SpVir and SpVic: rostral and caudal parts of the interpolar subdivision. Scale-bar: 1mm.

Supplementary Figure 7 Information processing along the low-level circuits of the rat’s whisking system.

Phase is extracted from the kinematics by follicle mechanoreceptors. Short-term depression (STD) of the synapses leading to both SpVir and PrVm entail high-pass filtering, while the synapses to PrVs retain low-frequency offset information (all-pass filtering). Hence, information on the fast whisker oscillations (rhythmic information) is passed to the paralemniscal pathway via the thalamic posteromedial complex (POm), while both the fast and the slow components of whisking are conveyed to the lemniscal pathway via the thalamic ventral posteromedial nucleus (VPM).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1192 kb)

Supplementary Methods Checklist

(PDF 384 kb)

Playback of awake whisking in anesthetized rats.

Tracking of whisker protraction angle in an awake, head-fixed rat (left, red) and generation of the same trajectory in an anesthetized rat (right, blue) using real-time closed-loop control. (MOV 1930 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wallach, A., Bagdasarian, K. & Ahissar, E. On-going computation of whisking phase by mechanoreceptors. Nat Neurosci 19, 487–493 (2016). https://doi.org/10.1038/nn.4221

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4221

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing