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.

  • Article
  • Published:

Elementary motion sequence detectors in whisker somatosensory cortex

Abstract

How the somatosensory cortex (S1) encodes complex patterns of touch, such as those that occur during tactile exploration, is poorly understood. In the mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to two-whisker sequences that involve the columnar whisker (CW) and one specific surround whisker (SW), usually in a SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW–CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, thus providing a basis for higher-order feature extraction.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: S1 neurons preferentially encode CW–SW sequences.
Fig. 2: Dense spatiotemporal mapping of CW–SW sequences reveals combination tuning in the S1.
Fig. 3: Nonlinear sharpening of combination tuning enhances rate coding for whisker combinations.
Fig. 4: SW-tuned units show strong rate coding for whisker combinations.
Fig. 5: Best combination responses at the best Δt are enhanced relative to a global sublinear scaling.
Fig. 6: Linear and nonlinear computations underlying combination tuning.
Fig. 7: Δt tuning and preference for tactile sequences inbound to the CW.
Fig. 8: Combination-selectivity measured in the S1 of awake mice.

Similar content being viewed by others

Data availability

Data are available in the Collaborative Research in Computational Neuroscience repository at https://crcns.org.

Code availability

The Matlab code for performing the statistical and data analyses are available upon request to the corresponding author.

References

  1. Sadagopan, S. & Wang, X. Nonlinear spectrotemporal interactions underlying selectivity for complex sounds in auditory cortex. J. Neurosci. 29, 11192–11202 (2009).

    Article  CAS  Google Scholar 

  2. Priebe, N. J. & Ferster, D. Mechanisms of neuronal computation in mammalian visual cortex. Neuron 75, 194–208 (2012).

    Article  CAS  Google Scholar 

  3. Stettler, D. D. & Axel, R. Representations of odor in the piriform cortex. Neuron 63, 854–864 (2009).

    Article  CAS  Google Scholar 

  4. Sachdev, R. N., Sellien, H. & Ebner, F. Temporal organization of multi-whisker contact in rats. Somatosens. Mot. Res. 18, 91–100 (2001).

    Article  CAS  Google Scholar 

  5. Grant, R. A., Mitchinson, B., Fox, C. W. & Prescott, T. J. Active touch sensing in the rat: anticipatory and regulatory control of whisker movements during surface exploration. J. Neurophysiol. 101, 862–874 (2009).

    Article  Google Scholar 

  6. Hobbs, J. A., Towal, R. B. & Hartmann, M. J. Z. Spatiotemporal patterns of contact across the rat vibrissal array during exploratory behavior. Front. Behav. Neurosci. 9, 356 (2016).

    Article  Google Scholar 

  7. Estebanez, L., Ferezou, I., Ego-Stengel, V. & Shulz, D. E. Representation of tactile scenes in the rodent barrel cortex. Neuroscience 368, 81–94 (2018).

    Article  CAS  Google Scholar 

  8. Jadhav, S. P., Wolfe, J. & Feldman, D. E. Sparse temporal coding of elementary tactile features during active whisker sensation. Nat. Neurosci. 12, 792–800 (2009).

    Article  CAS  Google Scholar 

  9. Crochet, S., Poulet, J. F. A., Kremer, Y. & Petersen, C. C. H. Synaptic mechanisms underlying sparse coding of active touch. Neuron 69, 1160–1175 (2011).

    Article  CAS  Google Scholar 

  10. Barth, A. L. & Poulet, J. F. Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 35, 345–355 (2012).

    Article  CAS  Google Scholar 

  11. Moore, C. I. & Nelson, S. B. Spatio-temporal subthreshold receptive fields in the vibrissa representation of rat primary somatosensory cortex. J. Neurophysiol. 80, 2882–2892 (1998).

    Article  CAS  Google Scholar 

  12. Borst, J. G. & Sakmann, B. Depletion of calcium in the synaptic cleft of a calyx-type synapse in the rat brainstem. J. Physiol. 521, 123–133 (1999).

    Article  CAS  Google Scholar 

  13. Welker, C. Receptive fields of barrels in the somatosensory neocortex of the rat. J. Comp. Neurol. 166, 173–189 (1976).

    Article  CAS  Google Scholar 

  14. Simons, D. J. Response properties of vibrissa units in rat SI somatosensory neocortex. J. Neurophysiol. 41, 798–820 (1978).

    Article  CAS  Google Scholar 

  15. Sato, T. R., Gray, N. W., Mainen, Z. F. & Svoboda, K. The functional microarchitecture of the mouse barrel cortex. PLoS Biol. 5, e189 (2007).

    Article  Google Scholar 

  16. Clancy, K. B., Schnepel, P., Rao, A. T. & Feldman, D. E. Structure of a single whisker representation in layer 2 of mouse somatosensory cortex. J. Neurosci. 35, 3946–3958 (2015).

    Article  CAS  Google Scholar 

  17. Jacob, V., Le Cam, J., Ego-Stengel, V. & Shulz, D. E. Emergent properties of tactile scenes selectively activate barrel cortex neurons. Neuron 60, 1112–1125 (2008).

    Article  CAS  Google Scholar 

  18. Vilarchao, M. E., Estebanez, L., Shulz, D. E. & Ferezou, I. Supra-barrel distribution of directional tuning for global motion in the mouse somatosensory cortex. Cell Rep. 22, 3534–3547 (2018).

    Article  CAS  Google Scholar 

  19. Estebanez, L., El Boustani, S., Destexhe, A. & Shulz, D. E. Correlated input reveals coexisting coding schemes in a sensory cortex. Nat. Neurosci. 15, 1691–1699 (2012).

    Article  CAS  Google Scholar 

  20. Ramirez, A. et al. Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input. Nat. Neurosci. 17, 866–875 (2014).

    Article  CAS  Google Scholar 

  21. Brumberg, J. C., Pinto, D. J. & Simons, D. J. Spatial gradients and inhibitory summation in the rat whisker barrel system. J. Neurophysiol. 76, 130–140 (1996).

    Article  CAS  Google Scholar 

  22. Ghazanfar, A. A. & Nicolelis, M. A. Nonlinear processing of tactile information in the thalamocortical loop. J. Neurophysiol. 78, 506–510 (1997).

    Article  CAS  Google Scholar 

  23. Shimegi, S., Ichikawa, T., Akasaki, T. & Sato, H. Temporal characteristics of response integration evoked by multiple whisker stimulations in the barrel cortex of rats. J. Neurosci. 19, 10164–10175 (1999).

    Article  CAS  Google Scholar 

  24. Shimegi, S., Akasaki, T., Ichikawa, T. & Sato, H. Physiological and anatomical organization of multiwhisker response interactions in the barrel cortex of rats. J. Neurosci. 20, 6241–6248 (2000).

    Article  CAS  Google Scholar 

  25. Mirabella, G. Integration of multiple-whisker inputs in rat somatosensory cortex. Cereb. Cortex 11, 164–170 (2002).

    Article  Google Scholar 

  26. Ego-Stengel, V., E Souza, T., Jacob, V. & Shulz, D. E. Spatiotemporal characteristics of neuronal sensory integration in the barrel cortex of the rat. J. Neurophysiol. 93, 1450–1467 (2005).

    Article  Google Scholar 

  27. Le Cam, J., Estebanez, L., Jacob, V. & Shulz, D. E. Spatial structure of multiwhisker receptive fields in the barrel cortex is stimulus dependent. J. Neurophysiol. 106, 986–998 (2011).

    Article  Google Scholar 

  28. Vinje, W. E. & Gallant, J. Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000).

    Article  CAS  Google Scholar 

  29. Willmore, B. & Tolhurst, D. J. Characterizing the sparseness of neural codes. Netw. Comput. Neural Syst. 12, 255–270 (2001).

    Article  CAS  Google Scholar 

  30. Willmore, B. D., Mazer, J. A. & Gallant, J. L. Sparse coding in striate and extrastriate visual cortex. J. Neurophysiol. 105, 2907–2919 (2011).

    Article  Google Scholar 

  31. Silver, R. A. Neuronal arithmetic. Nat. Rev. Neurosci. 11, 474–489 (2010).

    Article  CAS  Google Scholar 

  32. Safaai, H., von Heimendahl, M., Sorando, J. M., Diamond, M. E. & Maravall, M. Coordinated population activity underlying texture discrimination in rat barrel cortex. J. Neurosci. 33, 5843–5855 (2013).

    Article  CAS  Google Scholar 

  33. Isett, B. R., Feasel, S. H., Lane, M. A. & Feldman, D. E. Slip-based coding of local shape and texture in mouse S1. Neuron 97, 418–433.e5 (2018).

    Article  CAS  Google Scholar 

  34. Boloori, A.-R. & Stanley, G. B. The dynamics of spatiotemporal response integration in the somatosensory cortex of the vibrissa system. J. Neurosci. 26, 3767–3782 (2006).

    Article  CAS  Google Scholar 

  35. Gabernet, L., Jadhav, S. P., Feldman, D. E., Carandini, M. & Scanziani, M. Somatosensory integration controlled by dynamic thalamocortical feed-forward inhibition. Neuron 48, 315–327 (2005).

    Article  CAS  Google Scholar 

  36. Hill, D. N., Bermejo, R., Zeigler, H. P. & Kleinfeld, D. Biomechanics of the vibrissa motor plant in rat: rhythmic whisking consists of triphasic neuromuscular activity. J. Neurosci. 28, 3438–3455 (2008).

    Article  CAS  Google Scholar 

  37. Pei, Y.-C. & Bensmaia, S. J. The neural basis of tactile motion perception. J. Neurophysiol. 112, 3023–3032 (2014).

    Article  Google Scholar 

  38. Simons, D. J. Temporal and spatial integration in the rat SI vibrissa cortex. J. Neurophysiol. 54, 615–635 (1985).

    Article  CAS  Google Scholar 

  39. Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2012).

    Article  CAS  Google Scholar 

  40. Schiller, J., Major, G., Koester, H. J. & Schiller, Y. NMDA spikes in basal dendrites of cortical pyramidal neurons. Nature 404, 285–289 (2000).

    Article  CAS  Google Scholar 

  41. Lavzin, M., Rapoport, S., Polsky, A., Garion, L. & Schiller, J. Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature 490, 397–401 (2012).

    Article  CAS  Google Scholar 

  42. Ego-Stengel, V., Le Cam, J. & Shulz, D. E. Coding of apparent motion in the thalamic nucleus of the rat vibrissal somatosensory system. J. Neurosci. 32, 3339–3351 (2012).

    Article  CAS  Google Scholar 

  43. Simons, D. J. & Carvell, G. E. Thalamocortical response transformation in the rat vibrissa/barrel system. J. Neurophysiol. 61, 311–330 (1989).

    Article  CAS  Google Scholar 

  44. Barlow, H. B. & Levick, W. R. The mechanism of directionally selective units in rabbit’s retina. J. Physiol. 178, 477–504 (1965).

    Article  CAS  Google Scholar 

  45. Mauss, A. S., Vlasits, A., Borst, A. & Feller, M. Visual circuits for direction selectivity. Annu. Rev. Neurosci. 40, 211–230 (2017).

    Article  CAS  Google Scholar 

  46. Gruntman, E., Romani, S. & Reiser, M. B. Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila. Nat. Neurosci. 21, 250–257 (2018).

    Article  CAS  Google Scholar 

  47. Hassenstein, B. & Reichardt, W. Systemtheoretische analyse der zeit-, reihenfolgen- und vorzeichenauswertung bei der bewegungsperzeption des rüsselkäfers Chlorophanus. Z. Naturforsch. B 11, 513–524 (1956).

    Article  Google Scholar 

  48. Livingstone, M. S. Mechanisms of direction selectivity in macaque V1. Neuron 20, 509–526 (1998).

    Article  CAS  Google Scholar 

  49. Mikami, A., Newsome, W. T. & Wurtz, R. H. Motion selectivity in macaque visual cortex. I. Mechanisms of direction and speed selectivity in extrastriate area MT. J. Neurophysiol. 55, 1308–1327 (2017).

    Article  Google Scholar 

  50. Lefort, S., Tomm, C., Floyd Sarria, J.-C. & Petersen, C. C. H. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61, 301–316 (2009).

    Article  CAS  Google Scholar 

  51. Petersen, R. S. et al. Diverse and temporally precise kinetic feature selectivity in the VPm thalamic nucleus. Neuron 60, 890–903 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  53. Ludwig, K. A. et al. Using a common average reference to improve cortical neuron recordings from microelectrode arrays. J. Neurophysiol. 101, 1679–1689 (2009).

    Article  Google Scholar 

  54. Fee, M. S., Mitra, P. P. & Kleinfeld, D. Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. J. Neurosci. Methods 69, 175–188 (1996).

    Article  CAS  Google Scholar 

  55. Laboy-Juarez, K., Ahn, S. & Feldman, D. E. A normalized template matching method for improving spike detection in extracellular voltage recordings. Preprint at https://doi.org/10.1101/445585 (2018).

  56. Shinomoto, S. In Analysis of Parallel Spike Trains (eds Grün, S. & Rotter, S) 21–35 (Springer, 2010).

  57. Rolls, E. T. & Tovee, M. J. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J. Neurophysiol. 73, 713–726 (1995).

    Article  CAS  Google Scholar 

  58. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 58, 289–300 (1995).

    Google Scholar 

  59. Antoine, M. W., Langberg, T., Schnepel, P. & Feldman, D. E. Increased excitation–inhibition ratio stabilizes synapse and circuit excitability in four autism mouse models. Neuron 101, 648–661.e4 (2019).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by NIH 1R37 NS092367 (to D.E.F.), a NSF Graduate Research Fellowship (to K.J.L.-J.) and a NIH F99/K00 award (to K.J.L.-J.). The authors thank the members of the Feldman Lab for insightful comments and suggestions to the manuscript. They also thank J. Benson and C. Shi for performing behavioral training, and H.-C. Wang for developing behavioral methods, for experiment 3.

Author information

Authors and Affiliations

Authors

Contributions

K.J.L.-J. designed, performed and analyzed the results of the experiments. T.L. performed the awake recordings. S.A. performed the spike sorting. D.E.F. supervised the project. K.J.L.-J. and D.E.F. wrote the paper.

Corresponding author

Correspondence to Daniel E. Feldman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Nature Neuroscience thanks Sylvain Crochet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Diverse single-whisker tuning in within the S1 column.

(a) Average PSTH response to single whisker deflections across all units (n = 142). (b) Top, single-whisker tuning curves of 6 example units that were recorded in the same penetration of the D1 column. Bottom, multiunit activity across layers, multiunit activity was the number of voltage transients that exceeded 3 standard deviations of baseline. Shaded regions are SEM. (c) Fraction of units that had their peak single-whisker response to CW deflections (CW-tuned units, n = 89) across layers. Error bars are SE of sample proportion. (d) Average single-whisker receptive fields of CW (n = 89) and SW-tuned (n = 53) units. Responses were normalized to each unit’s maximum spiking response and surround whiskers were ranked by response strength. Tuning was broader for SW-tuned units (1-factor ANOVA on ranked tuning curves, p=.0003). bSW is the best surround whisker.

Supplementary Figure 2 SW-tuned units have the sharpest CW-SW sequence tuning.

Tuning sharpness, quantified as lifetime sparseness, among different sets of pairwise whisker sequences. Within each set, sequences were ranked from strongest to weakest spiking response, and lifetime sparseness was calculated for increasing number of sequences ranked. Thus, tuning sharpness can be compared between the N best CW-SW sequences and the N best SW-SW sequences (at X=N on the x-axis). Shaded regions are SEM.

Supplementary Figure 3 Diversity of combination tuning in S1.

Combination tuning curves of 30 combination-selective units, chosen to be representative of the population. Asterisks denote peak responses. Best Δt’s are shown in the top left of each unit.

Supplementary Figure 4 Relationship between firing rate and combination selectivity (CSI).

(a) Mean relationship between CSI and evoked firing rate to the best combination stimulus (large circles: mean, bars: SEM), for all combination-selective (n = 187) and non-selective (n = 264) units in Experiment 2. For stimuli at best Δt. Small circles are individual units. (b) Distribution of mean evoked firing rate for best CW-SW combinations at best Δt. Each histogram bar corresponds to a firing rate bin from (a). (c) Mean peak-aligned combination-tuning curves for combination-selective units with low firing rate (<0.81 spikes/stim, corresponding to the first two bins in (a) and (b). N = 95 units. Left, tuning calculated from total spiking. Right, tuning calculated after subtraction of mean spontaneous firing rate of each unit. Grey, tuning observed after shuffling spike counts across stimuli and trials. (d), Same as (c), but for combination-selective high firing rate units (>= 0.81 spikes/stim, corresponding to bins 3–10 in (a) and (b). N = 92 units. (e), Same as (d), but for non-selective high firing rate units. N = 91 units. Shaded regions are SEM. Asterisks in ce indicate best combination.

Supplementary Figure 5 Somatotopic bias of combination tuning in S1.

(a) Average, rate normalized, combination tuning curves for combination-selective (n = 187) and non-selective units (n = 264). (b) Average combination selectivity across combination-selective units that prefer a specific CW-SW combination. Each point at each angle is the average CSI of units that prefer the corresponding CW-SW combination. Error bars are SEM. D, V, R and C indicate dorsal, ventral, rostral and caudal neighboring whiskers on the whisker pad.

Supplementary Figure 6 Properties of combination tuning across layers.

Data are for all whisker-responsive cells in each layer, not just for combination-selective units (L2/3: 108 units; L4: 157 units; L5: 141 units; L6: 45 units. (a) Log10 CSI across layers for combination-selective units. Open circles are individual units, red line is the mean and shaded region is the 95% confidence interval. Asterisks denote statistically significant differences (1-factor ANOVA, p=.0025). (b) Fraction of combination-selective units across layers. Error bars are SE of sample proportion. (c) Average polar tuning curves aligned to the best stimulus for each layer. Axes are in spikes per stimulus and are the same across all plots. Shaded regions are SEM.

Supplementary Figure 7 Full data distributions for linear and nonlinear components of combination tuning.

Left and right columns are combination-selective (n = 187) and non-selective units (n = 264). These are the individual data points underlying the analysis of linear and nonlinear components of combination tuning in Fig. 6. Each dot is one unit. (a) Combination-specific enhancement (positive) or suppression (negative), relative to the global 0.64x-sublinearity, as a function of CW-SW combination rank. Open circles are individual units, red line is mean and shaded region is 95% confidence intervals. (b) and (c) Same as (a) but for measured combination-evoked response (b) and linear predicted response (c). (d) Combination-specific enhancement or suppression for CW-SW combinations as a function of the rank of single-whisker SW stimulus.

Supplementary Figure 8 Rate coding for CW-SW sequence order in S1.

(a) Average performance of a neural population decoder that predicts sequence order (inbound -Δt vs. outbound +Δt) based on single-trial spiking activity. The decoder was constructed as in Fig. 3 and 4. For this analysis, –Δt was defined as the [−50 ms, −10 ms] range, and +Δt was defined as the [+10 ms, +50 ms] range. Only responses to best whisker combinations were used in this analysis. Performance of the order decoder was ~65% correct on hold-out trials for N=100 units, relative to chance performance of 50%. Shaded region is SD across 2500 bootstrap trials. (b) Decoding performance across different combination identities. The order of each CW-SW combination was decoded from units that had that combination as their best combination. S1 units tuned to different spatial CW-SW combinations provided equally accurate order decoding.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Laboy-Juárez, K.J., Langberg, T., Ahn, S. et al. Elementary motion sequence detectors in whisker somatosensory cortex. Nat Neurosci 22, 1438–1449 (2019). https://doi.org/10.1038/s41593-019-0448-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-019-0448-6

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