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Edge-orientation processing in first-order tactile neurons

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Abstract

A fundamental feature of first-order neurons in the tactile system is that their distal axon branches in the skin and forms many transduction sites, yielding complex receptive fields with many highly sensitive zones. We found that this arrangement constitutes a peripheral neural mechanism that allows individual neurons to signal geometric features of touched objects. Specifically, we observed that two types of first-order tactile neurons that densely innervate the glabrous skin of the human fingertips signaled edge orientation via both the intensity and the temporal structure of their responses. Moreover, we found that the spatial layout of a neuron's highly sensitive zones predicted its sensitivity to particular edge orientations. We submit that peripheral neurons in the touch-processing pathway, as with peripheral neurons in the visual-processing pathway, perform feature extraction computations that are typically attributed to neurons in the cerebral cortex.

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Figure 1: Stimuli and sensitivity maps.
Figure 2: Observed and predicted neural responses.
Figure 3: Orientation discrimination and speed effect.
Figure 4: Timing of action potentials.

Change history

  • 15 September 2014

    In the version of this supplementary file originally posted online, the raster image in Supplementary Figure 2a was left/right mirror-reversed. The error has been corrected in this file as of 15 September 2014.

References

  1. Bensmaia, S.J., Denchev, P.V., Dammann, J.F., Craig, J.C. & Hsiao, S.S. The representation of stimulus orientation in the early stages of somatosensory processing. J. Neurosci. 28, 776–786 (2008).

    Article  CAS  Google Scholar 

  2. Yau, J.M., Pasupathy, A., Fitzgerald, P.J., Hsiao, S.S. & Connor, C.E. Analogous intermediate shape coding in vision and touch. Proc. Natl. Acad. Sci. USA 106, 16457–16462 (2009).

    Article  CAS  Google Scholar 

  3. Fitzgerald, P.J., Lane, J.W., Thakur, P.H. & Hsiao, S.S. Receptive field properties of the macaque second somatosensory cortex: representation of orientation on different finger pads. J. Neurosci. 26, 6473–6484 (2006).

    Article  CAS  Google Scholar 

  4. Hubel, D.H. & Wiesel, T.N. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (Lond.) 195, 215–243 (1968).

    Article  CAS  Google Scholar 

  5. Shapley, R., Hawken, M. & Ringach, D.L. Dynamics of orientation selectivity in the primary visual cortex and the importance of cortical inhibition. Neuron 38, 689–699 (2003).

    Article  CAS  Google Scholar 

  6. Hsiao, S. Central mechanisms of tactile shape perception. Curr. Opin. Neurobiol. 18, 418–424 (2008).

    Article  CAS  Google Scholar 

  7. Ferster, D. & Miller, K.D. Neural mechanisms of orientation selectivity in the visual cortex. Annu. Rev. Neurosci. 23, 441–471 (2000).

    Article  CAS  Google Scholar 

  8. Gollisch, T. & Meister, M. Eye smarter than scientists believed: neural computations in circuits of the retina. Neuron 65, 150–164 (2010).

    Article  CAS  Google Scholar 

  9. Venkataramani, S. & Taylor, W.R. Orientation selectivity in rabbit retinal ganglion cells is mediated by presynaptic inhibition. J. Neurosci. 30, 15664–15676 (2010).

    Article  CAS  Google Scholar 

  10. Cauna, N. Nerve supply and nerve endings in Meissner's corpuscles. Am. J. Anat. 99, 315–350 (1956).

    Article  CAS  Google Scholar 

  11. Looft, F.J. Response of cat cutaneous mechanoreceptors to punctate and grating stimuli. J. Neurophysiol. 56, 208–220 (1986).

    Article  CAS  Google Scholar 

  12. Paré, M., Smith, A.M. & Rice, F.L. Distribution and terminal arborizations of cutaneous mechanoreceptors in the glabrous finger pads of the monkey. J. Comp. Neurol. 445, 347–359 (2002).

    Article  Google Scholar 

  13. Goldfinger, M.D. Random-sequence stimulation of the G1 hair afferent unit. Somatosens. Mot. Res. 7, 19–45 (1990).

    Article  CAS  Google Scholar 

  14. Brown, A.G. & Iggo, A. A quantitative study of cutaneous receptors and afferent fibres in the cat and rabbit. J. Physiol. (Lond.) 193, 707–733 (1967).

    Article  CAS  Google Scholar 

  15. Lesniak, D.R. et al. Computation identifies structural features that govern neuronal firing properties in slowly adapting touch receptors. eLife (Cambridge) 3, e01488 (2014).

    Article  Google Scholar 

  16. Vallbo, A.B., Olausson, H., Wessberg, J. & Kakuda, N. Receptive field characteristics of tactile units with myelinated afferents in hairy skin of human subjects. J. Physiol. (Lond.) 483, 783–795 (1995).

    Article  CAS  Google Scholar 

  17. Johansson, R.S. Tactile sensibility in the human hand: receptive field characteristics of mechanoreceptive units in the glabrous skin area. J. Physiol. (Lond.) 281, 101–125 (1978).

    Article  CAS  Google Scholar 

  18. Phillips, J.R., Johansson, R.S. & Johnson, K.O. Responses of human mechanoreceptive afferents to embossed dot arrays scanned across fingerpad skin. J. Neurosci. 12, 827–839 (1992).

    Article  CAS  Google Scholar 

  19. Johansson, R.S. & Flanagan, J.R. Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).

    Article  CAS  Google Scholar 

  20. Johnson, K.O. The roles and functions of cutaneous mechanoreceptors. Curr. Opin. Neurobiol. 11, 455–461 (2001).

    Article  CAS  Google Scholar 

  21. Johnson, K.O. & Lamb, G.D. Neural mechanisms of spatial tactile discrimination: neural patterns evoked by braille-like dot patterns in the monkey. J. Physiol. (Lond.) 310, 117–144 (1981).

    Article  CAS  Google Scholar 

  22. Vega-Bermudez, F., Johnson, K.O. & Hsiao, S.S. Human tactile pattern recognition: active versus passive touch, velocity effects and patterns of confusion. J. Neurophysiol. 65, 531–546 (1991).

    Article  CAS  Google Scholar 

  23. Blake, D.T., Johnson, K.O. & Hsiao, S.S. Monkey cutaneous SAI and RA responses to raised and depressed scanned patterns: effects of width, height, orientation, and a raised surround. J. Neurophysiol. 78, 2503–2517 (1997).

    Article  CAS  Google Scholar 

  24. Panzeri, S., Petersen, R.S., Schultz, S.R., Lebedev, M. & Diamond, M.E. The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron 29, 769–777 (2001).

    Article  CAS  Google Scholar 

  25. Gollisch, T. & Meister, M. Rapid neural coding in the retina with relative spike latencies. Science 319, 1108–1111 (2008).

    Article  CAS  Google Scholar 

  26. Johansson, R.S. & Birznieks, I. First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat. Neurosci. 7, 170–177 (2004).

    Article  CAS  Google Scholar 

  27. Vázquez, Y., Salinas, E. & Romo, R. Transformation of the neural code for tactile detection from thalamus to cortex. Proc. Natl. Acad. Sci. USA 110, E2635–E2644 (2013).

    Article  Google Scholar 

  28. Schreiber, S., Fellous, J.M., Whitmer, D., Tiesinga, P. & Sejnowski, T.J. A new correlation-based measure of spike timing reliability. Neurocomputing 52–54, 925–931 (2003).

    Article  Google Scholar 

  29. Fellous, J.M., Tiesinga, P.H., Thomas, P.J. & Sejnowski, T.J. Discovering spike patterns in neuronal responses. J. Neurosci. 24, 2989–3001 (2004).

    Article  CAS  Google Scholar 

  30. Hyvärinen, J. & Poranen, A. Movement-sensitive and direction and orientation-selective cutaneous receptive fields in the hand area of the post-central gyrus in monkeys. J. Physiol. (Lond.) 283, 523–537 (1978).

    Article  Google Scholar 

  31. Warren, S., Hamalainen, H.A. & Gardner, E.P. Objective classification of motion- and direction-sensitive neurons in primary somatosensory cortex of awake monkeys. J. Neurophysiol. 56, 598–622 (1986).

    Article  CAS  Google Scholar 

  32. Pei, Y.C., Hsiao, S.S., Craig, J.C. & Bensmaia, S.J. Neural mechanisms of tactile motion integration in somatosensory cortex. Neuron 69, 536–547 (2011).

    Article  CAS  Google Scholar 

  33. Khalsa, P.S., Friedman, R.M., Srinivasan, M.A. & Lamotte, R.H. Encoding of shape and orientation of objects indented into the monkey fingerpad by populations of slowly and rapidly adapting mechanoreceptors. J. Neurophysiol. 79, 3238–3251 (1998).

    Article  CAS  Google Scholar 

  34. Phillips, J.R. & Johnson, K.O. Tactile spatial resolution. III. A continuum mechanics model of skin predicting mechanoreceptor responses to bars, edges, and gratings. J. Neurophysiol. 46, 1204–1225 (1981).

    Article  CAS  Google Scholar 

  35. Goodwin, A.W. & Wheat, H.E. Sensory signals in neural populations underlying tactile perception and manipulation. Annu. Rev. Neurosci. 27, 53–77 (2004).

    Article  CAS  Google Scholar 

  36. Vega-Bermudez, F. & Johnson, K.O. SA1 and RA receptive fields, response variability, and population responses mapped with a probe array. J. Neurophysiol. 81, 2701–2710 (1999).

    Article  CAS  Google Scholar 

  37. Loomis, J.M. Tactile pattern perception. Perception 10, 5–27 (1981).

    Article  CAS  Google Scholar 

  38. Vega-Bermudez, F. & Johnson, K.O. Surround suppression in the responses of primate SA1 and RA mechanoreceptive afferents mapped with a probe array. J. Neurophysiol. 81, 2711–2719 (1999).

    Article  CAS  Google Scholar 

  39. Eagles, J.P. & Purple, R.L. Afferent fibers with multiple encoding sites. Brain Res. 77, 187–193 (1974).

    Article  CAS  Google Scholar 

  40. Lindblom, Y. & Tapper, D.N. Integration of impulse activity in a peripheral sensory unit. Exp. Neurol. 15, 63–69 (1966).

    Article  CAS  Google Scholar 

  41. Branco, T. & Häusser, M. The single dendritic branch as a fundamental functional unit in the nervous system. Curr. Opin. Neurobiol. 20, 494–502 (2010).

    Article  CAS  Google Scholar 

  42. Britten, K.H., Shadlen, M.N., Newsome, W.T. & Movshon, J.A. The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745–4765 (1992).

    Article  CAS  Google Scholar 

  43. Georgopoulos, A.P., Schwartz, A.B. & Kettner, R.E. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

    Article  CAS  Google Scholar 

  44. Birznieks, I., Jenmalm, P., Goodwin, A.W. & Johansson, R.S. Encoding of direction of fingertip forces by human tactile afferents. J. Neurosci. 21, 8222–8237 (2001).

    Article  CAS  Google Scholar 

  45. Stanley, G.B. Reading and writing the neural code. Nat. Neurosci. 16, 259–263 (2013).

    Article  CAS  Google Scholar 

  46. Gire, D.H. et al. Temporal processing in the olfactory system: can we see a smell? Neuron 78, 416–432 (2013).

    Article  CAS  Google Scholar 

  47. Panzeri, S., Brunel, N., Logothetis, N.K. & Kayser, C. Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 33, 111–120 (2010).

    Article  CAS  Google Scholar 

  48. König, P., Engel, A.K. & Singer, W. Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci. 19, 130–137 (1996).

    Article  Google Scholar 

  49. Usrey, W.M. The role of spike timing for thalamocortical processing. Curr. Opin. Neurobiol. 12, 411–417 (2002).

    Article  CAS  Google Scholar 

  50. Vallbo, A.B. & Hagbarth, K.E. Activity from skin mechanoreceptors recorded percutaneously in awake human subjects. Exp. Neurol. 21, 270–289 (1968).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank B. Edin and D. Wolpert for their helpful comments on previous versions of this manuscript. We thank A. Bäckström, C. Hjältén, E. Jarocka, P. Jenmalm, P. Utsi and G. Westling for their technical and logistical support. This work was funded by the Swedish Research Council Projects 08667 and 22209, as well as by the Strategic Research Program in Neuroscience at the Karolinska Institute. J.A.P. received a long-term fellowship from the Human Frontier Science Program.

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Correspondence to J Andrew Pruszynski.

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Integrated supplementary information

Supplementary Figure 1 Experimental setup and basic methodology.

(a,b) Participants sat with their palm facing upwards and their fingers firmly attached to a table. We recorded action potentials from single first-order tactile neurons innervating the fingertips by inserting an electrode (‘E’ in a) into the median nerve at the level of the upper arm wrist50. The stimulating surface, wrapped around a rotating drum, moved across the neuron’s receptive field (blue mark in b) along the proximal-distal axis of the finger. (c) The stimulating surface consisted of various embossed elements (gray areas) including three small dots, lines with different orientations, and different shapes including triangles, diamonds and squares.

Supplementary Figure 2 Intensity of a neuron’s response signals line orientation.

(a) Top, spatial event plot focusing on the line stimuli and sensitivity maps for an exemplar FA-1 neurons (calibration bar = 1 mm). Color code above the line stimuli indicates complementary edge orientations. Bottom, the black and gray lines represent the neuron’s firing rate profiles for all line orientations. Format as in Fig. 2d. (b) Same format as a for an exemplar SA-1 neuron. (c) Relationship between peak firing rates for line stimuli with complementary orientations for FA-1 (n = 26) and SA-1 (n = 21) neurons, respectively. All dots are above the diagonal because the stronger response is shown along the vertical axis. Greater distances from the diagonal indicate stronger effects of edge orientation on the peak firing rate. Filled dots indicate a significant difference between complementary orientations (P < 0.05, one-sample, two-tailed, t-test corrected for 141 (47 neurons x 3 complementary pairs) comparisons, n = 12 per line orientation; see Methods). The diamonds indicate the exemplar neurons shown in a and b. Inset, mean difference (+1 s.e.m.) in peak firing rate for the complementary orientations for significant differences. (d) ROC curves illustrating the discrimination capacity of the exemplar neurons shown in a (top) and b (bottom) for each of the three complementary pairs. Lines dashed if the area under the ROC curve did not exceed 0.75. (e) ROC curves illustrating discrimination capacity across all FA-1 (n = 26) and SA-1 (n = 21) neurons for all line orientation pairs (color-coded). Lines gray if the area under the ROC curve did not exceed 0.75. (f) ROC area across neurons for each pair of complementary line orientations. The horizontal white line indicates the median value and the extent of the box depicts the interquartile range.

Supplementary Figure 3 Classification details.

Confusion matrices based on peak firing rate (a) or firing rate profile (b) for the matched speed condition (top) and the 30 mm/s drum speed (bottom). Each matrix shows how true edge orientations were classified as a percentage of the total number of samples. Elements with black backgrounds indicate correct classification. Bold numbers show the pairs of true and classified edge orientations that occurred more often than expected by chance. Briefly, we estimated the probability of randomly placing a finite number of samples (speed matched: 14 neurons x 12 repeats = 168 samples; 30 mm/s drum speed: 47 x 12 = 564 samples) into one of seven elements using a bootstrap procedure with 10000 repeats. In the limit, the result is clearly 14.2% (i.e. 1 / 7) but the number of samples determines the confidence interval. In our case, the 99th percentile of assignments was 21% and 18% for the speed matched and 30 mm/s drum speed data, respectively.

Supplementary Figure 4 Spike timing analysis for an exemplar FA-1 neuron.

(a) Top, instantaneous firing rate in response to the indicated line stimuli and the neuron’s sensitivity map. Bottom, raster plots showing responses to the 12 individual passes of the stimulus where each dot represents an action potential. (b) Action potentials were convolved with Gaussian kernels (s.d. = 0.5, 1, 2, 4, 8, 16 and 32 ms) to investigate the importance of precise spike timing for signaling line orientation information. Traces show probability density for the occurrence of action potentials as a function of time for each kernel computed by averaging the 12 responses after convolution. Note that increased kernel-widths gradually abolished the detailed temporal structure of the response. (c) Average correlation coefficients (± 1 s.d.) obtained when correlating each response to the vertical line stimulus (0°) with each response to every other line stimulus as a function of kernel width. For narrow kernels, the highest correlation clearly occurred when vertical line stimuli were compared to other vertical line stimuli. With wider kernels, correlation coefficients between the vertical line stimuli and the other stimuli were more similar. For this particular neuron, the 2 ms filter yielded the best classification results on average (red traces in b,c). (d) Same layout as c but for all line stimuli and the 2 ms kernel. Note that the highest correlations coefficients occurred when a given line orientation was compared to itself (dashed line).

Supplementary Figure 5 Effect of edge orientation and speed on spike timing.

(a) Raster plots and probability density (2 ms kernel) for the occurrence of action potentials shown as a function of drum position for all seven line orientations and four speed conditions for an exemplar SA-1 neuron (#33, see also Fig. 3). Sensitivity map generated at the 30 mm/s drum speed (calibration bar = 2 mm). Note the similarity of the firing probability profiles as a function of speed in contrast to the substantial changes that occur as a function of orientation. Asterisks indicate speed-matched conditions. (b) Same format as a for an exemplar FA-1 neuron (#40).

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Pruszynski, J., Johansson, R. Edge-orientation processing in first-order tactile neurons. Nat Neurosci 17, 1404–1409 (2014). https://doi.org/10.1038/nn.3804

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