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:

A somatotopic map of vibrissa motion direction within a barrel column

Nature Neuroscience volume 9, pages 543–551 (2006)Cite this article

Abstract

Most mammals possess high-resolution visual perception, with primary visual cortices containing fine-scale, inter-related feature representations (for example, orientation and ocular dominance). Rats lack precise vision, but their vibrissa sensory system provides a precise tactile modality, including vibrissa-related 'barrel' columns in primary somatosensory cortex. Here, we examined the subcolumnar organization of direction preference and somatotopy using a new omni-directional, multi-vibrissa stimulator. We discovered a direction map that was systematically linked to somatotopy, such that neurons were tuned for motion toward their preferred surround vibrissa. This sub-barrel column direction map demonstrated an emergent refinement from layer IV to layer II/III. These data suggest that joint processing of multiple sensory features is a common property of high-resolution sensory systems.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: A sub-barrel direction map in anatomic coordinates.
Figure 2: Analysis of the direction map for subpopulations of the data.
Figure 3: Rostral-caudal direction preference maps.
Figure 4: Direction preference maps in layers IV and II/III for regular- and fast-spiking units.
Figure 5: Direction preference maps in layers IV and II/III for all units.
Figure 6: Consistency in direction preference across laminae predicts the correlation of direction tuning and somatotopy.

Similar content being viewed by others

References

  1. Head, H. Studies in Neurology (Oxford Univ. Press, London, 1920).

    Google Scholar 

  2. Merzenich, M.M. & Kaas, J.H. Principles of organization of sensory-perceptual systems in mammals. Progress in Psychobiology and Physiological Psychology Vol. 9, 1–42 (Academic Press, San Diego, 1980).

    Google Scholar 

  3. Hubel, D.H., Wiesel, T.N. & LeVay, S. Functional architecture of area 17 in normal and monocularly deprived macaque monkeys. Cold Spring Harb. Symp. Quant. Biol. 40, 581–589 (1976).

    Article  CAS  Google Scholar 

  4. Hubener, M., Shoham, D., Grinvald, A. & Bonhoeffer, T. Spatial relationships among three columnar systems in cat area 17. J. Neurosci. 17, 9270–9284 (1997).

    Article  CAS  Google Scholar 

  5. Blasdel, G.G. Orientation selectivity, preference, and continuity in monkey striate cortex. J. Neurosci. 12, 3139–3161 (1992).

    Article  CAS  Google Scholar 

  6. Ohki, K., Chung, S., Ch'ng, Y.H., Kara, P. & Reid, R.C. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433, 597–603 (2005).

    Article  CAS  Google Scholar 

  7. Yu, H., Farley, B.J., Jin, D.Z. & Sur, M. The coordinated mapping of visual space and response features in visual cortex. Neuron 47, 267–280 (2005).

    Article  CAS  Google Scholar 

  8. Bosking, W.H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997).

    Article  CAS  Google Scholar 

  9. Carreira-Perpinan, M.A. & Goodhill, G.J. Influence of lateral connections on the structure of cortical maps. J. Neurophysiol. 92, 2947–2959 (2004).

    Article  Google Scholar 

  10. Ruthazer, E.S. & Stryker, M.P. The role of activity in the development of long-range horizontal connections in area 17 of the ferret. J. Neurosci. 16, 7253–7269 (1996).

    Article  CAS  Google Scholar 

  11. Godecke, I., Kim, D.S., Bonhoeffer, T. & Singer, W. Development of orientation preference maps in area 18 of kitten visual cortex. Eur. J. Neurosci. 9, 1754–1762 (1997).

    Article  CAS  Google Scholar 

  12. Prusky, G.T., West, P.W. & Douglas, R.M. Behavioral assessment of visual acuity in mice and rats. Vision Res. 40, 2201–2209 (2000).

    Article  CAS  Google Scholar 

  13. Girman, S.V., Sauve, Y. & Lund, R.D. Receptive field properties of single neurons in rat primary visual cortex. J. Neurophysiol. 82, 301–311 (1999).

    Article  CAS  Google Scholar 

  14. Carvell, G.E. & Simons, D.J. Biometric analyses of vibrissal tactile discrimination in the rat. J. Neurosci. 10, 2638–2648 (1990).

    Article  CAS  Google Scholar 

  15. Ahissar, E. & Arieli, A. Figuring space by time. Neuron 32, 185–201 (2001).

    Article  CAS  Google Scholar 

  16. 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 

  17. 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 

  18. Polley, D.B., Rickert, J.L. & Frostig, R.D. Whisker-based discrimination of object orientation determined with a rapid training paradigm. Neurobiol. Learn. Mem. 83, 134–142 (2005).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. Woolsey, T.A. & Van der Loos, H. The structural organization of layer IV in the somatosensory region (SI) of mouse cerebral cortex. The description of a cortical field composed of discrete cytoarchitectonic units. Brain Res. 17, 205–242 (1970).

    Article  CAS  Google Scholar 

  21. Armstrong-James, M., Fox, K. & Das-Gupta, A. Flow of excitation within rat barrel cortex on striking a single vibrissa. J. Neurophysiol. 68, 1345–1358 (1992).

    Article  CAS  Google Scholar 

  22. Abbott, L.F. & Nelson, S.B. Synaptic plasticity: taming the beast. Nat. Neurosci. 3, 1178–1183 (2000).

    Article  CAS  Google Scholar 

  23. Allen, C.B., Celikel, T. & Feldman, D.E. Long-term depression induced by sensory deprivation during cortical map plasticity in vivo. Nat. Neurosci. 6, 291–299 (2003).

    Article  CAS  Google Scholar 

  24. Waite, P.M. & Jacquin, M.F. Dual innervation of the rat vibrissa: responses of trigeminal ganglion cells projecting through deep or superficial nerves. J. Comp. Neurol. 322, 233–245 (1992).

    Article  CAS  Google Scholar 

  25. Wineski, L.E. Facial morphology and vibrissal movement in the golden hamster. J. Morphol. 183, 199–217 (1985).

    Article  CAS  Google Scholar 

  26. Bruno, R.M., Khatri, V., Land, P.W. & Simons, D.J. Thalamocortical angular tuning domains within individual barrels of rat somatosensory cortex. J. Neurosci. 23, 9565–9574 (2003).

    Article  CAS  Google Scholar 

  27. Bruno, R.M. & Simons, D.J. Feedforward mechanisms of excitatory and inhibitory cortical receptive fields. J. Neurosci. 22, 10966–10975 (2002).

    Article  CAS  Google Scholar 

  28. Zar, J.H. Biostatistical Analysis (Prentice-Hall, Upper Saddle River, New Jersey, 1996).

    Google Scholar 

  29. 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 

  30. Brecht, M., Roth, A. & Sakmann, B. Dynamic receptive fields of reconstructed pyramidal cells in layers 3 and 2 of rat somatosensory barrel cortex. J. Physiol. (Lond.) 553, 243–265 (2003).

    Article  CAS  Google Scholar 

  31. Armstrong-James, M. & Fox, K. Spatiotemporal convergence and divergence in the rat S1 “barrel” cortex. J. Comp. Neurol. 263, 265–281 (1987).

    Article  CAS  Google Scholar 

  32. Swadlow, H.A. & Gusev, A.G. Receptive-field construction in cortical inhibitory interneurons. Nat. Neurosci. 5, 403–404 (2002).

    Article  CAS  Google Scholar 

  33. 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  Google Scholar 

  34. Chklovskii, D.B. Synaptic connectivity and neuronal morphology: two sides of the same coin. Neuron 43, 609–617 (2004).

    CAS  PubMed  Google Scholar 

  35. Laughlin, S.B. & Sejnowski, T.J. Communication in neuronal networks. Science 301, 1870–1874 (2003).

    Article  CAS  Google Scholar 

  36. Benison, A.M., Ard, T.D., Crosby, A.M. & Barth, D.S. Temporal patterns of field potentials in vibrissa/barrel cortex reveal stimulus orientation and shape. J. Neurophysiol. published online January 4, 2006 (doi:10.1152/jn.01034.2005).

  37. Lichtenstein, S.H., Carvell, G.E. & Simons, D.J. Responses of rat trigeminal ganglion neurons to movements of vibrissae in different directions. Somatosens. Mot. Res. 7, 47–65 (1990).

    Article  CAS  Google Scholar 

  38. Lee, S.H. & Simons, D.J. Angular tuning and velocity sensitivity in different neuron classes within layer 4 of rat barrel cortex. J. Neurophysiol. 91, 223–229 (2004).

    Article  Google Scholar 

  39. Neimark, M.A., Andermann, M.L., Hopfield, J.J. & Moore, C.I. Vibrissa resonance as a transduction mechanism for tactile encoding. J. Neurosci. 23, 6499–6509 (2003).

    Article  CAS  Google Scholar 

  40. Moore, C.I. & Andermann, M.L. The vibrissa resonance hypothesis. Somatosensory Plasticity Ch. 2, 21–60 (CRC Press, Nashville, Tennessee).

  41. Hartmann, M.J., Johnson, N.J., Towal, R.B. & Assad, C. Mechanical characteristics of rat vibrissae: resonant frequencies and damping in isolated whiskers and in the awake behaving animal. J. Neurosci. 23, 6510–6519 (2003).

    Article  CAS  Google Scholar 

  42. Brecht, M., Preilowski, B. & Merzenich, M.M. Functional architecture of the mystacial vibrissae. Behav. Brain Res. 84, 81–97 (1997).

    Article  CAS  Google Scholar 

  43. Land, P.W. & Erickson, S.L. Subbarrel domains in rat somatosensory (S1) cortex. J. Comp. Neurol. 490, 414–426 (2005).

    Article  Google Scholar 

  44. Timofeeva, E., Merette, C., Emond, C., Lavallee, P. & Deschenes, M. A map of angular tuning preference in thalamic barreloids. J. Neurosci. 23, 10717–10723 (2003).

    Article  CAS  Google Scholar 

  45. Wilent, W.B. & Contreras, D. Stimulus-dependent changes in spike threshold enhance feature selectivity in rat barrel cortex neurons. J. Neurosci. 25, 2983–2991 (2005).

    Article  CAS  Google Scholar 

  46. Huang, W., Armstrong-James, M., Rema, V., Diamond, M.E. & Ebner, F.F. Contribution of supragranular layers to sensory processing and plasticity in adult rat barrel cortex. J. Neurophysiol. 80, 3261–3271 (1998).

    Article  CAS  Google Scholar 

  47. Diamond, M.E., Huang, W. & Ebner, F.F. Laminar comparison of somatosensory cortical plasticity. Science 265, 1885–1888 (1994).

    Article  CAS  Google Scholar 

  48. Polley, D.B., Kvasnak, E. & Frostig, R.D. Naturalistic experience transforms sensory maps in the adult cortex of caged animals. Nature 429, 67–71 (2004).

    Article  CAS  Google Scholar 

  49. Stern, E.A., Maravall, M. & Svoboda, K. Rapid development and plasticity of layer 2/3 maps in rat barrel cortex in vivo. Neuron 31, 305–315 (2001).

    Article  CAS  Google Scholar 

  50. Bender, K.J., Rangel, J. & Feldman, D.E. Development of columnar topography in the excitatory layer 4 to layer 2/3 projection in rat barrel cortex. J. Neurosci. 23, 8759–8770 (2003).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank J. Ritt, A. Nelson, M. Sur, C. Reid, R. Desimone and R. Born for feedback, and K. Kempadoo and A. Ramanathan for histology. We thank the US National Institutes of Health, the National Science Foundation, the McGovern Institute for Brain Research (C.I.M.) and the Howard Hughes Medical Institute (M.L.A.) for supporting this work.

Author information

Authors and Affiliations

  1. Harvard Program in Biophysics, Medical School Campus, Building C-2 Room 122, 240 Longwood Avenue, Boston, 02115, Massachusetts, USA

    Mark L Andermann

  2. McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, 46-2171 Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Christopher I Moore

Authors
  1. Mark L Andermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christopher I Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher I Moore.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Methodological Approaches A. Left: A photograph of the stimulator employed. Movements were driven by piezoelectric elements poled for operation in 2 axes: Combinations of these axes through a pair of driving signals and piezoelectric amplifiers dedicated to each stimulator allowed omni-directional control. The label 'a' denotes the piezoelectric element, and 'b' the custom capillary vibrissa holder. B. A cartoon of the 4-tetrode arrays showing the aspect ratio of the electrodes. C. A reconstruction of a D3 barrel showing two reconstructed electrode positions from a single penetration of the array. The bounding square and bisecting lines show the parameters recorded for reconstruction and alignment of barrels across experiments. D. A bar plot showing the fall-off in peak action potential amplitude across contacts on a tetrode. When a significant trigger was recorded, the contact with the largest amplitude spike was identified, and the peak amplitude of the spike signal within .5msec on the other contacts calculated. Data shown were taken from single-units (N = 420) to ensure accurate isolation. Spike amplitudes reduced to 53% at an inter-contact distance of 35m and 41% at 50m. Because of the stringent cut-off applied for acceptance spikes (5 standard deviations above background), these findings suggest that most neurons were located less than ˜50m from the most proximal contact. (PDF 659 kb)

Supplementary Fig. 2

Correlation Between Somatotopy and Anatomic Position within a Barrel Column The relation between the anatomic barrel position and somatotopic center of mass are plotted for the anterior-posterior axis (top) and inferior-superior axis (bottom). Symbols/colors indicate individual experiments. A significant correlation was observed between somatotopy and anatomic position along the anterior-posterior axis in layers IV (r = .44, p < .0001, N = 71) and II/III (r = .34, p = .004, N = 58). A significant correlation was also observed between somatotopy and anatomic position along the inferior-superior axis in layers IV (r = .34, p = .002, N = 71) and II/III (r = .56, p = .0001, N = 58). Note, however, that the range of somatotopic values along the inferior-superior axis is considerably truncated. (PDF 235 kb)

Supplementary Fig. 3

Direction Preference Analysis for Multi-Unit Maps of Layers IV and II/III Left Somatotopic direction maps, angular prediction calculations and quartile distributions of tuning values for multi-unit activity (MUA: See Figures 2, 4 and 5 in paper and accompany text). Data were culled from 111 recording sites in layer II/III and 112 in layer IV (N/quartile = 28 for both). The non-rotated map revealed a significant difference in direction preferences between 1st and 4th quartiles in layer II/III (p = .0024, difference in mean rostral-caudal preference = .41) and a marginal association in layer IV (p = .09, .23). After rotation to the optimal axis (see text), these differences increased and were significant in both laminae (layer II/III: -500 rotation, p < .0001, .68; layer IV: -430 rotation, p = .009, .37). (PDF 311 kb)

Supplementary Fig. 4

Scatter Plots Showing the Association of Direction Preference Across Laminae Plots are shown for the 3 cell groupings analyzed, All neurons (including those unclassified: N = 149), RSUs (N = 36) and FSUs (N = 31). For all plots, the unity line and lines bounding a 450 window of similarity are shown. Red dots indicate neuron pairs that showed < +450 difference in tuning. As shown in Figure 6A, RSUs revealed a significant sub-population (18/36 pairs; 50% of the sample; chance = 25%) that demonstrated less than or equal to a 450 difference in tuning across layers. This trend was present but weaker in the All neurons grouping and absent in the FSUs. Angular-angular correlations were weak but significant for RSUs (r = .06, p < .001) and All neurons (r = .001, p < .001) but were not significant for FSUs (r = .0007, p > .05) (PDF 135 kb)

Supplementary Fig. 5

Shifts in Direction Preference Across Laminae for RSUs and FSUs Maps are shown of shifts in direction preference for RSUs and FSUs for non-columnar cell pairs (left) and columnar (right) (see Figure 6 in paper and text). Scatter plots are also shown relating shift in direction preference to the layer II/III somatotopic position. As with analysis of All neurons (Figure 6C), these sub-populations showed a systematic pattern of shifts for non-columnar cell pairs, with anterior somatotopic tuning positions shifting rostral, and posterior somatotopic recordings shifting caudal. Linear regressions fit to non-columnar positions (black line and points) and columnar positions (gray line and points) showed a significant correlation for non-columnar sites (RSUs: r = .65, p = .002, N = 18; FSUs: r = .52, p = .007, N = 22) but not for columnar sites (RSUs: r = -.09, p = .35, N = 18; FSUs: r = -.17, p = .33, N = 9). (PDF 137 kb)

Supplementary Fig. 6

An Idealized Model of Direction Representation in Layers IV and II/III The data presented support the model shown, in which an outwardly radiating direction preference map is observed in the supragranular layers that is dominated by rostral and caudal direction preferences. This map emerges from a more disorganized preference distribution within layer IV. Across layers, those RSUs that demonstrated a correlation between direction preference and somatotopy showed columnar organization. Arrows and red and blue colors indicate the rostral or caudal direction preference of a given spatial position. Dashed gray arrows indicate the prediction that direction maps in surrounding barrel columns will demonstrate contrasting direction tuning at the somatotopic border between the two vibrissa representations. (PDF 299 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Andermann, M., Moore, C. A somatotopic map of vibrissa motion direction within a barrel column. Nat Neurosci 9, 543–551 (2006). https://doi.org/10.1038/nn1671

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1671

This article is cited by

Search

Advanced 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