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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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.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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. d–f, 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).
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. c–e, 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 discussion of frequency selective tuning of S1 neurons and of forelimb Pacinian corpuscles as the source of the fS1 vibration responses; associated supplementary references.
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.
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.
Optogenetic stimulation of mouse forepaw during cortical imaging.
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). https://doi.org/10.1038/s41586-019-1015-8
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