Correlated input reveals coexisting coding schemes in a sensory cortex

Journal name:
Nature Neuroscience
Volume:
15,
Pages:
1691–1699
Year published:
DOI:
doi:10.1038/nn.3258
Received
Accepted
Published online

Abstract

As in other sensory modalities, one function of the somatosensory system is to detect coherence and contrast in the environment. To investigate the neural bases of these computations, we applied different spatiotemporal patterns of stimuli to rat whiskers while recording multiple neurons in the barrel cortex. Model-based analysis of the responses revealed different coding schemes according to the level of input correlation. With uncorrelated stimuli on 24 whiskers, we identified two distinct functional categories of neurons, analogous in the temporal domain to simple and complex cells of the primary visual cortex. With correlated stimuli, however, a complementary coding scheme emerged: two distinct cell populations, similar to reinforcing and antagonist neurons described in the higher visual area MT, responded specifically to correlations. We suggest that similar context-dependent coexisting coding strategies may be present in other sensory systems to adapt sensory integration to specific stimulus statistics.

At a glance

Figures

  1. Barrel cortex neurons encode whisker deflections in a common low-dimensional subspace.
    Figure 1: Barrel cortex neurons encode whisker deflections in a common low-dimensional subspace.

    (a) Uncorrelated Gaussian white noise was applied to 24 whiskers on the rat right whisker pad in the rostro-caudal axis. (b) Example of a significant linear filter showing whisker displacement over time for a mono-vibrissal neuron obtained using STC analysis. (c) PCA over all significant linear filters obtained among all protocols for mono-vibrissal neurons (680 filters), with two dominant eigenvalues explaining 73% of the variance (blue and red points). Dashed line represents mean + three s.d. significance threshold. (d) Corresponding common filters, together with the Hilbert transform of the red filter (black dashed line). (e) Normalized significant filters of all neurons and protocols, projected onto the stimulus subspace spanned by the common filters. The radius corresponds to the similarity of the individual filter with common filters 1 and 2.

  2. STC analysis reveals simple and complex nonlinear functions in response to spatially uncorrelated stimuli.
    Figure 2: STC analysis reveals simple and complex nonlinear functions in response to spatially uncorrelated stimuli.

    (a) Top, linear-nonlinear model schematic for a simple neuron. Bottom, nonlinear function of a simple neuron, depicting the neuron firing rate in the common subspace (4,436 spikes). Marginal distributions are shown on the graph sides. AP, action potentials (b) Data are presented as in a for a complex neuron (24,641 spikes). (c) PSTH and raster plot of the response of the simple neuron shown in a to a sinusoidal stimulus (shown below the raster plot). (d) Data are presented as in c for the complex neuron shown in b. (e) Comparison for a neuron population (N = 26) between the modulation index R1/R0 measured using sinusoidal stimulus (ordinate) and the prediction derived from the linear-nonlinear models (abscissa). (f) Population histogram of the modulation index R1/R0 as predicted from all recorded local neurons using their linear-nonlinear models (N = 286).

  3. Neurons' nonlinear functions depend on interwhisker instantaneous correlation.
    Figure 3: Neurons' nonlinear functions depend on interwhisker instantaneous correlation.

    (a) Stimuli with interwhisker correlation c were built by weighted summation of independent and common Gaussian noise stimulations. (b) The bimodal z score population distribution obtained with uncorrelated (and correlated) Gaussian noise defines local (dark gray) and global (light gray) neurons among all responsive neurons (N = 222). Red dashed line represents 1% significance threshold. (c) Nonlinear function of a layer V global neuron for five increasing levels of interwhisker correlation (gray to black). Spike counts from uncorrelated to correlated: 9,815, 10,148, 10,694, 10,863 and 11,089 spikes. (d) Marginal nonlinear functions for the different levels of correlation along common filters 1 (left) and 2 (right). (e,f) Data presented as in c and d for a layer V local neuron (spike counts from uncorrelated to correlated: 2,317, 1,968, 1,787, 1,363 and 1,040 spikes). Stim., stimuli; norm., normalized.

  4. Center-surround tuning maps of local neurons reveals preference for antagonist center-surround stimulations.
    Figure 4: Center-surround tuning maps of local neurons reveals preference for antagonist center-surround stimulations.

    (a) Left, identical Gaussian noise was applied to all whiskers except the center whisker, which was stimulated with a different Gaussian noise waveform. Spike-triggered center and surround stimulus segments were collected. Right, the phase of both center and surround in the common subspace was computed for every spike and allowed the construction of a center/surround nonlinear function, the phase tuning map presented in b. (b) Center-surround phase tuning map of a simple neuron (4,159 spikes). The green line represents correlated stimuli and the blue lines represent antagonist stimuli. Left, marginal surround phase tuning. Top, marginal center phase tuning. Dashed line represents phase tuning curve obtained using uncorrelated stimulation as in Figure 1a. Bottom left, green tilted graph indicates phase tuning curve obtained with a fully correlated stimulation. (c) Population distribution of the preferred phase difference between center and surround for neurons showing a significant center and surround phase tuning (N = 35 of 91, Rayleigh test, P < 0.05).

  5. Confirmation of the antagonist tuning of local neurons.
    Figure 5: Confirmation of the antagonist tuning of local neurons.

    (a) Center-surround tuning map of a simple local neuron (1,216 spikes). Green and blue lines represent correlated and antagonist stimulus subspace, respectively. (b) Common filter 1 playback, correlated (left), antagonist (right), and center whisker only (middle). (c) Raster plots and corresponding PSTHs for correlated (light green), antagonist (light blue) and center-only (light red) playback. Dark curves represent corresponding linear-nonlinear model predictions. (d) Data presented as in c for opposite center phase. (e) Response reliability in correlated versus antagonist stimuli for two opposite center phases (c and d in a, N = 9). (f) Reliability for correlated (corr.), uncorrelated (uncorr.) and anti-phased (anticorr.) stimuli for the same population (N = 9). Uncorrelated stimulation reliability was significantly higher than correlated reliability (Wilcoxon paired test, *P < 0.05), but significantly lower than anti-phased reliability (Wilcoxon paired test, **P < 0.01). Error bars represent s.d. (g) Uncorrelated center-surround (top) and correlated (bottom) natural whisker deflections used in i and j. (h) Auto-correlogram of natural (top) and Gaussian (middle) stimulus (stim.). Bottom, cross-correlogram (cross-corr.) of center versus surround stimuli. (i) Goodness-of-fit of the natural center-surround stimulus PSTHs with a center-surround (CS) model versus center-only model (N = 16, Wilcoxon paired test, P < 0.02). (j) Goodness-of-fit of the PSTH recorded during the natural center-surround stimulus and during the correlated natural stimulus, using the same center-surround model (same population as in i, Wilcoxon paired test, P > 0.5).

  6. Confirmation of the functional properties of global neurons.
    Figure 6: Confirmation of the functional properties of global neurons.

    (a) Nonlinear function (projected onto common filter 1 dimension) of a global neuron obtained during uncorrelated (gray, 26,193 spikes) and correlated (black, 26,322 spikes) stimulations. Color-coded (orange and purple) waveforms on the bottom sides of the graph indicate the direction in the filter 1 subspace (as in b and c). (b) Top, spatially uncorrelated playback of the first common filter in both directions (orange and purple) with an ISI drawn from a Poisson distribution (see Online Methods). Bottom, corresponding most responsive PSTH among all whiskers for both stimulus directions. (c) Data are presented as in b for a spatially correlated stimulus. (d) Population analysis of the PSTH z score for uncorrelated versus correlated stimulations. Dashed lines represent 5% significance thresholds.

  7. Center and surround phase tuning with temporal delays.
    Figure 7: Center and surround phase tuning with temporal delays.

    (a) Phase tuning maps for center and surround stimulations were computed for −40-ms to +10-ms windows that were shifted with various delays between the spike time and the center (δtcenter) or surround (δtsurround) stimulations. (b) Resulting center and surround phase tuning maps for different delays (δtcenter, δtsurround) for a local simple cell (4,159 spikes). (c) Neuron firing rate for δtcenter = 0 and various surround delays when considering identical center and surround stimulation phases (that is, the diagonal of the tuning map along the δtcenter = 0 column shown in green, corresponding to correlated center-surround stimulations). (d) Population distribution of the significant positive (N = 55) and negative (N = 40) surround delays across all neurons that display facilitation (N = 67). Average negative (–8.6 ms) and positive (9 ms) delays are indicated with black arrows. The preferred delays for e (−8 ms and +7.6 ms) and f (−8 ms and +8 ms) are shown as blue and purple arrows, respectively. (e) Center (C)-surround (S) ISI curves derived from b for 16 different center and surround stimulations (tuning map diagonal in green). Continuous blue line represents baseline activity. Dashed lines represent maximal firing rate to center-only stimulations. (f) Data are presented as in e for a second case study.

References

  1. Carvell, G.E. & Simons, D.J. Biometric analyses of vibrissal tactile discrimination in the rat. J. Neurosci. 10, 26382648 (1990).
  2. Simons, D.J. Response properties of vibrissa units in rat SI somatosensory neocortex. J. Neurophysiol. 41, 798820 (1978).
  3. Brecht, M. & Sakmann, B. Dynamic representation of whisker deflection by synaptic potentials in spiny stellate and pyramidal cells in the barrels and septa of layer 4 rat somatosensory cortex. J. Physiol. (Lond.) 543, 4970 (2002).
  4. Moore, C.I. & Nelson, S.B. Spatio-temporal subthreshold receptive fields in the vibrissa representation of rat primary somatosensory cortex. J. Neurophysiol. 80, 28822892 (1998).
  5. Wilent, W.B. & Contreras, D. Dynamics of excitation and inhibition underlying stimulus selectivity in rat somatosensory cortex. Nat. Neurosci. 8, 13641370 (2005).
  6. 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, 65106519 (2003).
  7. Ritt, J.T., Andermann, M.L. & Moore, C.I. Embodied information processing: vibrissa mechanics and texture features shape micromotions in actively sensing rats. Neuron 57, 599613 (2008).
  8. Ego-Stengel, V., Souza, T.M., Jacob, V. & Shulz, D.E. Spatiotemporal characteristics of neuronal sensory integration in the barrel cortex of the rat. J. Neurophysiol. 93, 14501467 (2005).
  9. 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, 1016410175 (1999).
  10. Simons, D.J. & Carvell, G.E. Thalamocortical response transformation in the rat vibrissa/barrel system. J. Neurophysiol. 61, 311330 (1989).
  11. Ghazanfar, A.A. & Nicolelis, M.A. Spatiotemporal properties of layer V neurons of the rat primary somatosensory cortex. Cereb. Cortex 9, 348361 (1999).
  12. Hirata, A. & Castro-Alamancos, M.A. Cortical transformation of wide-field (multiwhisker) sensory responses. J. Neurophysiol. 100, 358370 (2008).
  13. Mirabella, G., Battiston, S. & Diamond, M.E. Integration of multiple-whisker inputs in rat somatosensory cortex. Cereb. Cortex 11, 164170 (2001).
  14. Brumberg, J.C., Pinto, D.J. & Simons, D.J. Spatial gradients and inhibitory summation in the rat whisker barrel system. J. Neurophysiol. 76, 130140 (1996).
  15. Schwartz, O., Pillow, J.W., Rust, N.C. & Simoncelli, E.P. Spike–triggered neural characterization. J. Vis. 6, 484507 (2006).
  16. de Ruyter van Steveninck, R. & Bialek, W. Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences. Proc. R. Soc. Lond. B Biol. Sci. 234, 379414 (1988).
  17. de Kock, C.P. & Sakmann, B. Spiking in primary somatosensory cortex during natural whisking in awake head-restrained rats is cell-type specific. Proc. Natl. Acad. Sci. USA 106, 1644616450 (2009).
  18. Geffen, M.N., Broome, B.M., Laurent, G. & Meister, M. Neural encoding of rapidly fluctuating odors. Neuron 61, 570586 (2009).
  19. Maravall, M., Petersen, R.S., Fairhall, A.L., Arabzadeh, E. & Diamond, M.E. Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex. PLoS Biol. 5, e19 (2007).
  20. Skottun, B.C. et al. Classifying simple and complex cells on the basis of response modulation. Vision Res. 31, 10791086 (1991).
  21. Pettigrew, J.D., Nikara, T. & Bishop, P. Responses to moving slits by single units in cat striate cortex. Exp. Brain Res. 6, 373390 (1968).
  22. Lee, S.H. & Simons, D. Angular tuning and velocity sensitivity in different neuron classes within layer 4 of rat barrel cortex. J. Neurophysiol. 91, 223229 (2004).
  23. Ghazanfar, A.A. & Nicolelis, M.A. Nonlinear processing of tactile information in the thalamocortical loop. J. Neurophysiol. 78, 506510 (1997).
  24. Born, R.T. & Tootell, R.B. Segregation of global and local motion processing in primate middle temporal visual area. Nature 357, 497499 (1992).
  25. Jadhav, S.P., Wolfe, J. & Feldman, D.E. Sparse temporal coding of elementary tactile features during active whisker sensation. Nat. Neurosci. 12, 792800 (2009).
  26. Shimegi, S., Akasaki, T., Ichikawa, T. & Sato, H. Physiological and anatomical organization of multi-whisker response interactions in the barrel cortex of rats. J. Neurosci. 20, 62416248 (2000).
  27. Simons, D.J. Temporal and spatial integration in the rat SI vibrissa cortex. J. Neurophysiol. 54, 615635 (1985).
  28. Jacob, V., Cam, J.L., Ego-Stengel, V. & Shulz, D.E. Emergent properties of tactile scenes selectively activate barrel cortex neurons. Neuron 60, 11121125 (2008).
  29. Born, R.T. Center–surround interactions in the middle temporal visual area of the owl monkey. J. Neurophysiol. 84, 26582669 (2000).
  30. Sincich, L.C., Park, K.F., Wohlgemuth, M.J. & Horton, J.C. Bypassing V1: a direct geniculate input to area MT. Nat. Neurosci. 7, 11231128 (2004).
  31. Pei, Y.C., Hsiao, S.S. & Bensmaia, S.J. The tactile integration of local motion cues is analogous to its visual counterpart. Proc. Natl. Acad. Sci. USA 105, 81308135 (2008).
  32. Pei, Y.-C., Hsiao, S.S., Craig, J.C. & Bensmaia, S.J. Neural mechanisms of tactile motion integration in somatosensory cortex. Neuron 69, 536547 (2011).
  33. Ferezou, I. et al. Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice. Neuron 56, 907923 (2007).
  34. Matyas, F. et al. Motor control by sensory cortex. Science 330, 12401243 (2010).
  35. Jones, H.E., Wang, W. & Sillito, A.M. Spatial organization and magnitude of orientation contrast interactions in primate V1. J. Neurophysiol. 88, 27962808 (2002).
  36. Newsome, W.T., Wurtz, R.H., Dürsteler, M.R. & Mikami, A. Deficits in visual motion processing following ibotenic acid lesions of the middle temporal visual area of the macaque monkey. J. Neurosci. 5, 825840 (1985).
  37. Rust, N.C., Schwartz, O., Movshon, J.A. & Simoncelli, E.P. Spatiotemporal elements of macaque v1 receptive fields. Neuron 46, 945956 (2005).
  38. 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, 33393351 (2012).
  39. Aguilar, J.R. & Castro-Alamancos, M. Spatiotemporal gating of sensory inputs in thalamus during quiescent and activated states. J. Neurosci. 25, 1099011002 (2005).
  40. Roy, N.C., Bessaih, T. & Contreras, D. Comprehensive mapping of whisker-evoked responses reveals broad, sharply tuned thalamocortical input to layer 4 of barrel cortex. J. Neurophysiol. 105, 24212437 (2011).
  41. Petersen, R.S. et al. Diverse and temporally precise kinetic feature selectivity in the VPm thalamic nucleus. Neuron 60, 890903 (2008).
  42. Le Cam, J., Estebanez, L., Jacob, V. & Shulz, D.E. Spatial structure of multi-whisker receptive fields in the barrel cortex is stimulus dependent. J. Neurophysiol. 106, 986998 (2011).
  43. Zhu, J.J. & Connors, B.W. Intrinsic firing patterns and whisker-evoked synaptic responses of neurons in the rat barrel cortex. J. Neurophysiol. 81, 11711183 (1999).
  44. Touryan, J., Felsen, G. & Dan, Y. Spatial structure of complex cell receptive fields measured with natural images. Neuron 45, 781791 (2005).
  45. Friedberg, M.H., Lee, S.M. & Ebner, F.F. Modulation of receptive field properties of thalamic somatosensory neurons by the depth of anesthesia. J. Neurophysiol. 81, 22432252 (1999).
  46. Jacob, V. et al. The Matrix: a new tool for probing the whisker-to-barrel system with natural stimuli. J. Neurosci. Methods 189, 6574 (2010).
  47. Wolfe, J. et al. Texture coding in the rat whisker system: slip-stick versus differential resonance. PLoS Biol. 6, e215 (2008).
  48. DiCarlo, J.J., Lane, J.W., Hsiao, S.S. & Johnson, K.O. Marking microelectrode penetrations with fluorescent dyes. J. Neurosci. Methods 64, 7581 (1996).
  49. Cardona, A. et al. An integrated micro- and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 8, e1000502 (2010).
  50. Smyth, D., Willmore, B., Baker, G.E., Thompson, I.D. & Tolhurst, D.J. The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation. J. Neurosci. 23, 47464759 (2003).

Download references

Author information

  1. These authors contributed equally to this work.

    • Luc Estebanez &
    • Sami El Boustani

Affiliations

  1. Unité de Neurosciences, Information et Complexité, UPR 3293, Centre National de la Recherche Scientifique, Gif sur Yvette, France.

    • Luc Estebanez,
    • Sami El Boustani,
    • Alain Destexhe &
    • Daniel E Shulz
  2. Institut de Biologie de l'École Normale Supérieure, Institut National de la Santé et de la Recherche Médicale U1024, Centre National de la Recherche Scientifique UMR8197, Paris, France.

    • Luc Estebanez

Contributions

L.E. and S.E.B. devised the protocols and analyzed the data with D.E.S. and A.D. L.E. and S.E.B. carried out the extracellular recordings. D.E.S. and A.D. supervised the research. L.E., S.E.B., A.D. and D.E.S. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (3M)

    Supplementary Figures 1–12 and Supplementary Table 1

Additional data