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Feature-selective encoding of substrate vibrations in the forelimb somatosensory cortex


The spectral content of skin vibrations, produced by either displacing the finger across a surface texture1 or passively sensing external movements through the solid substrate2,3, provides fundamental information about our environment. Low-frequency flutter (below 50 Hz) applied locally to the primate fingertip evokes cyclically entrained spiking in neurons of the primary somatosensory cortex (S1), and thus spike rates in these neurons increase linearly with frequency4,5. However, the same local vibrations at high frequencies (over 100 Hz) cannot be discriminated on the basis of differences in discharge rates of S1 neurons4,6, because spiking is only partially entrained at these frequencies6. Here we investigated whether high-frequency substrate vibrations applied broadly to the mouse forelimb rely on a different cortical coding scheme. We found that forelimb S1 neurons encode vibration frequency similarly to sound pitch representation in the auditory cortex7,8: their spike rates are selectively tuned to a preferred value of a low-level stimulus feature without any temporal entrainment. This feature, identified as the product of frequency and a power function of amplitude, was also found to be perceptually relevant as it predicted behaviour in a frequency discrimination task. Using histology, peripheral deafferentation and optogenetic receptor tagging, we show that these selective responses are inherited from deep Pacinian corpuscles located adjacent to bones, most densely around the ulna and radius and only sparsely along phalanges. This mechanoreceptor arrangement and the tuned cortical rate code suggest that the mouse forelimb constitutes a sensory channel best adapted for passive ‘listening’ to substrate vibrations, rather than for active texture exploration.

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Fig. 1: Feature-selective tuning to forelimb vibrations in mouse S1.
Fig. 2: Perceptual relevance of feature-selective neuronal tuning.
Fig. 3: Spikes of fS1 neurons are not cyclically entrained by forelimb vibrations.
Fig. 4: Identifying the origin of fS1 responses in forelimb mechanoreceptors.

Data availability

All analysed data in the current study are available from the corresponding author upon reasonable request. The figures that have associated raw data are: Figs. 1e, 2a, b, e, f, 3e, 4c–e and Extended Data Figs. 1, 6a.


  1. Shao, Y., Hayward, V. & Visell, Y. Spatial patterns of cutaneous vibration during whole-hand haptic interactions. Proc. Natl Acad. Sci. USA 113, 4188–4193 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Narins, P. M., Meenderink, S. W. F., Tumulty, J. P., Cobo-Cuan, A. & Márquez, R. Plant-borne vibrations modulate calling behaviour in a tropical amphibian. Curr. Biol. 28, R1333–R1334 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. O’Connell-Rodwell, C. E. Keeping an ‘ear’ to the ground: seismic communication in elephants. Physiology 22, 287–294 (2007).

    Article  PubMed  Google Scholar 

  4. Mountcastle, V. B., Talbot, W. H., Sakata, H. & Hyvärinen, J. Cortical neuronal mechanisms in flutter-vibration studied in unanesthetized monkeys. Neuronal periodicity and frequency discrimination. J. Neurophysiol. 32, 452–484 (1969).

    Article  CAS  PubMed  Google Scholar 

  5. Salinas, E., Hernandez, A., Zainos, A. & Romo, R. Periodicity and firing rate as candidate neural codes for the frequency of vibrotactile stimuli. J. Neurosci. 20, 5503–5515 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Harvey, M. A., Saal, H. P., Dammann, J. F., III & Bensmaia, S. J. Multiplexing stimulus information through rate and temporal codes in primate somatosensory cortex. PLoS Biol. 11, e1001558 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Montgomery, N. & Wehr, M. Auditory cortical neurons convey maximal stimulus-specific information at their best frequency. J. Neurosci. 30, 13362–13366 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tao, C. et al. Bidirectional shifting effects of the sound intensity on the best frequency in the rat auditory cortex. Sci. Rep. 7, 44493 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Butts, D. A. How much information is associated with a particular stimulus? Network 14, 177–187 (2003).

    Article  PubMed  Google Scholar 

  11. Butts, D. A. & Goldman, M. S. Tuning curves, neuronal variability, and sensory coding. PLoS Biol. 4, e92 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Arabzadeh, E., Panzeri, S. & Diamond, M. E. Whisker vibration information carried by rat barrel cortex neurons. J. Neurosci. 24, 6011–6020 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Arabzadeh, E., Petersen, R. S. & Diamond, M. E. Encoding of whisker vibration by rat barrel cortex neurons: implications for texture discrimination. J. Neurosci. 23, 9146–9154 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Stevens, S. S. The relation of pitch to intensity. J. Acoust. Soc. Am. 6, 150–154 (1935).

    Article  ADS  Google Scholar 

  15. Zwislocki, J. J. & Nguyen, M. Place code for pitch: a necessary revision. Acta Otolaryngol. 119, 140–145 (1999).

    Article  CAS  PubMed  Google Scholar 

  16. Johansson, R. S., Landström, U. & Lundström, R. Responses of mechanoreceptive afferent units in the glabrous skin of the human hand to sinusoidal skin displacements. Brain Res. 244, 17–25 (1982).

    Article  CAS  PubMed  Google Scholar 

  17. Abraira, V. E. & Ginty, D. D. The sensory neurons of touch. Neuron 79, 618–639 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Bell, J., Bolanowski, S. & Holmes, M. H. The structure and function of pacinian corpuscles: a review. Prog. Neurobiol. 42, 79–128 (1994).

    Article  CAS  PubMed  Google Scholar 

  19. Kumamoto, K., Senuma, H., Ebara, S. & Matsuura, T. Distribution of pacinian corpuscles in the hand of the monkey, Macaca fuscata. J. Anat. 183, 149–154 (1993).

    PubMed Central  PubMed  Google Scholar 

  20. Hunt, C. C. On the nature of vibration receptors in the hind limb of the cat. J. Physiol. 155, 175–186 (1961).

    Article  CAS  Google Scholar 

  21. Zelená, J. The role of sensory innervation in the development of mechanoreceptors. Prog. Brain Res. 43, 59–64 (1976).

    Article  PubMed  Google Scholar 

  22. Manfredi, L. R. et al. The effect of surface wave propagation on neural responses to vibration in primate glabrous skin. PLoS One 7, e31203 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kumamoto, K., Takei, M., Kinoshita, M., Ebara, S. & Matsuura, T. Distribution of pacinian corpuscles in the cat forefoot. J. Anat. 182, 23–28 (1993).

    PubMed Central  PubMed  Google Scholar 

  24. Curthoys, I. S. et al. The basis for using bone-conducted vibration or air-conducted sound to test otolithic function. Ann. NY Acad. Sci. 1233, 231–241 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Rado, R., Terkel, J. & Wollberg, Z. Seismic communication signals in the blind mole-rat (Spalax ehrenbergi): electrophysiological and behavioral evidence for their processing by the auditory system. J. Comp. Physiol. A 183, 503–511 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Reuter, T., Nummela, S. & Hemilä, S. Elephant hearing. J. Acoust. Soc. Am. 104, 1122–1123 (1998).

    Article  ADS  CAS  PubMed  Google Scholar 

  27. Sherrick, C. E., Cholewiak, R. W. & Collins, A. A. The localization of low- and high-frequency vibrotactile stimuli. J. Acoust. Soc. Am. 88, 169–179 (1990).

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Talbot, W. H., Darian-Smith, I., Kornhuber, H. H. & Mountcastle, V. B. The sense of flutter-vibration: comparison of the human capacity with response patterns of mechanoreceptive afferents from the monkey hand. J. Neurophysiol. 31, 301–334 (1968).

    Article  CAS  PubMed  Google Scholar 

  29. Andermann, M. L., Ritt, J., Neimark, M. A. & Moore, C. I. Neural correlates of vibrissa resonance; band-pass and somatotopic representation of high-frequency stimuli. Neuron 42, 451–463 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Delmas, P., Hao, J. & Rodat-Despoix, L. Molecular mechanisms of mechanotransduction in mammalian sensory neurons. Nat. Rev. Neurosci. 12, 139–153 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Madisen, L. et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat. Neurosci. 15, 793–802 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Taniguchi, H. et al. A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex. Neuron 71, 995–1013 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fleming, M. S. et al. A RET-ER81-NRG1 signaling pathway drives the development of pacinian corpuscles. J. Neurosci. 36, 10337–10355 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pease, D. C. & Quilliam, T. A. Electron microscopy of the pacinian corpuscle. J. Biophys. Biochem. Cytol. 3, 331–342 (1957).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pawson, L., Slepecky, N. B. & Bolanowski, S. J. Immunocytochemical identification of proteins within the Pacinian corpuscle. Somatosens. Mot. Res. 17, 159–170 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Pnevmatikakis, E. A. et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285–299 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schütt, H. H., Harmeling, S., Macke, J. H. & Wichmann, F. A. Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data. Vision Res. 122, 105–123 (2016).

    Article  PubMed  Google Scholar 

  38. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    MATH  Google Scholar 

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We thank V. Hayward and S. Bensmaia for advice and comments on the manuscript; R. Zimmerman and G. Galiñanes for help with histology; S. Crochet for advice on electrophysiology experiments; and C. Bonardi for help with training mice on the forelimb reaching task. This work was supported by the Swiss National Science Foundation (PP00P3_133710), the European Research Council (OPTOMOT), the New York Stem Cell Foundation and the International Foundation for Paraplegia Research. D.H. is a New York Stem Cell Foundation-Robertson Investigator.

Author information

Authors and Affiliations



M.P. and D.H. conceptualized the study. M.P., D.H. and K.M. designed experiments. M.P., K.M. and G.C. ran experiments and analysed data. M.P. and D.H. wrote the manuscript.

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Correspondence to Daniel Huber.

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Extended data figures and tables

Extended Data Fig. 1 Spectral analysis of measured displacement demonstrates that vibrations were pure sinusoids.

a, Example of a displacement measured by the stimulator’s strain gauge (black) of a single 590-Hz vibration. The bottom trace shows an expansion in time of the interval highlighted in red. b, Amplitude spectrum of the displacement produced by the 590-Hz stimulus indicates that the physical vibration consisted of a single frequency component (that is, was a pure sinusoid). Components apparent at higher frequencies were highly attenuated relative to the stimulation frequency and were for the most part continuously present, as revealed by the signal spectrum of the pre-stimulus baseline period (blue). c, Analogous amplitude spectra were obtained for stimuli at all tested frequencies and amplitudes.

Source data

Extended Data Fig. 2 Feature-selective tuning across different vibration amplitudes.

a–c, Left, frequency-selective tuning curves of example neurons for vibration amplitudes of 5.45 µm (a), 1.08 µm (b) and 0.23 µm (c) (grey dots, individual stimulus responses; grey traces, mean values; black traces, descriptive function fits; vertical lines, best frequencies). Right, normalized tuning curves for all neurons with identifiable curve peaks sorted by increasing best frequency. Grey histograms are distributions of neurons’ best frequencies at the three tested amplitudes. Note the different tested frequency ranges.

Extended Data Fig. 3 Tuning curve peaks (best frequencies) are identifiable as the tested frequency range is extended at lower amplitudes and maximum SSI is conveyed at the peaks of the tuning curves.

a, Frequency tuning of four example neurons at amplitude 5.45 µm that appear to show monotonic or saturating increases. b, When tested over a wider frequency range at attenuated amplitudes, these neurons also show frequency-selective tuning. Top, middle and bottom panels are tuning curves of the same five example neurons tested at vibration amplitudes of 5.45 µm, 1.08 µm and 0.23 µm, respectively. c, Frequency-tuned responses of three representative neurons (bottom) tested at the indicated vibration amplitudes. Their respective bias-corrected SSI (top) shows information maxima at the tuning curve peaks (*P < 0.01, permutation test). d, Normalized tuned responses (grey) and their mean (red) of frequency-selective neurons across the three amplitudes (n = 548) aligned to their best frequency (bottom) and the corresponding significant SSI values (grey squares), their mean (black line) and their proportions (red circle sizes) relative to the total number of neurons (top). All other symbols are as in Fig. 1.

Extended Data Fig. 4 Entrainment analysis of fS1 neuron spikes for different stimulus periods and durations.

a, Same data as in Fig. 3e separately analysed for the first and last 125 ms of the stimulus. b, Same data for six neurons recorded at stimulus durations of 0.5 s and 1 s. c, Same data as in b separately analysed for the first 200 ms of the stimulus. d, Same data as in b separately analysed for the stimulus period following the first 200 ms. All symbols are as in Fig. 3e.

Extended Data Fig. 5 PC location in deep forelimb tissue and frequency-dependent attenuation of fS1 responses after nerve block at the wrist.

a, Locations of identified PCs in three additional mouse forelimbs, showing a highly consistent distribution across mice. b, Example H&E-stained sections of fingers (left) and forelimb (right) showing that PCs (arrows) are located closer to bones than to skin in both cases. c, Example responses of six fS1 neurons showing no changes in tuning after saline injections (top) and heterogenous frequency-dependent changes in tuning after blocking nerve transmission from the forepaw (bottom).

Extended Data Fig. 6 Auditory stimulation and forelimb movements do not account for the feature-selective responses in fS1.

a, To test for the possibility that airborne sounds activated the imaged neurons in fS1, the forepaw was moved off the stimulator. To test for the possibility that vibrations propagated through the body to the cochlea, loud white noise was used to mask this hypothetical stimulation. The response change ratios (median ± quartiles) relative to the control condition (zero level) averaged across all frequencies with significant activity increases in the control condition show that with the forepaw off the stimulator, sound alone could not drive increases in Δf/f0 (P < 10−19, two-sided Wilcoxon signed-rank test, z = −9.22, n = 113 cells, 9 mice) and that the white noise mask had only a small aversive effect on the responses (P = 0.006, z = −2.76, n = 128 cells, 9 mice; dots, single neurons; squares, median ± quartiles). Significant increases in Δf/f0 could be evoked in none of the individual neurons by sound alone at any frequency (P < 0.01, randomization test). b, A representative fS1 neuron activated by forepaw vibrations (black, control condition) was not responsive to sound alone (green, forepaw off condition) and unaffected by the auditory mask (blue, white noise condition). Top, denoised Δf/f0 responses evoked by 730-Hz and 1,220-Hz vibrations (thin lines, individual stimulus responses; thick lines, mean response). Bottom, frequency tuning curves tested with four vibration amplitudes, as depicted by the greyscale bar, to cover the extended range of frequencies (dots, individual responses; lines, mean response). Inset, cropped two-photon image depicting the same responding neuron in both conditions (yellow arrow). c, To test whether stimulus-locked motor events could drive neural responses in fS1, we trained mice to reach with their forelimb for the water reward following the series of forepaw vibrations on each trial. df, Representative neurons responding to reach onset (d), target onset (e) or around the time of water consumption (f) but not to forepaw vibrations. Left and middle, denoised Δf/f0 responses to reaching and a chosen vibration frequency, respectively (thin lines, individual stimulus responses; thick lines, mean response; green shading in e, range of reach onset times). Right, frequency tuning (dots, individual responses; lines, mean response). g, Representative neuron responding to forepaw vibrations but not forelimb movements. h, Representative neuron responding to both events but not showing frequency-tuned activity. i, Left, median peak Δf/f0 during reaching versus at preferred vibration frequency for all imaged neurons (n = 156 cells, 3 mice, 8 FOVs). Inset, enlarged view of the data in the black square. Right, depiction of numbers of neurons responding to vibrotactile stimulation (red cross), reaching onset, target onset or water consumption (blue symbols) or to both events (magenta symbols).

Source data

Extended Data Fig. 7 Analysis of optogenetic forepaw stimulation data and result summary.

a, Δf/f0 traces of an example neuron on two successive optogenetic stimulation trials (blue lines) with different paw placements relative to the optical fibre (blue filled circles). b, Concatenated horizontal and vertical means of forepaw images immediately before stimulus onset were projected into a low-dimensional space using t-SNE. The formed clusters correspond to stimuli with highly similar paw placements relative to the optical fibre and were used to subjectively estimate locations of the optogenetic stimulation on the paw, as illustrated. ce, Δf/f0 responses for each neuron were used to colour code the t-SNE projection points. The clearly visible clustering of high versus low Δf/f0 values implies that stimulus locations on the paw are separable based on the size of Δf/f0 responses they evoke. The black squares denote clusters in t-SNE space used to evaluate average responses (indicated numerical values) evoked by stimulus locations shown in Fig. 4l. f, Out of 50 neurons that responded to forelimb illumination of the ‘PC optimal’ forelimb site and 62 that responded to forelimb vibration, 12 were activated by both stimuli (5 mice). g, Same data as in f but showing less neuronal overlap in the case of forepaw optogenetic stimulation.

Supplementary information

Supplementary Information

Supplementary discussion of frequency selective tuning of S1 neurons and of forelimb Pacinian corpuscles as the source of the fS1 vibration responses; associated supplementary references.

Reporting Summary

Video 1

Vibratory stimulation of mouse forelimb during cortical imaging. Successive vibrations are delivered to the forelimb of a mouse followed by water reward. The numerical values indicate the moment of stimulation and the vibration frequency.

Video 2

Vibratory stimulation of mouse forelimb followed by goal-directed reaching. Successive vibrations are delivered to the forelimb of a mouse followed by reaching for a water droplet. The numerical values indicate the moment of stimulation and the vibration frequency.

Video 3

Optogenetic stimulation of mouse forepaw during cortical imaging.

Video 4

Monitoring of paw placement during optogenetic stimulation.

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Prsa, M., Morandell, K., Cuenu, G. et al. Feature-selective encoding of substrate vibrations in the forelimb somatosensory cortex. Nature 567, 384–388 (2019).

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