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.

  • Letter
  • Published:

Nonlinear dendritic integration of sensory and motor input during an active sensing task

Nature volume 492, pages 247–251 (2012)Cite this article

Subjects

Abstract

Active dendrites provide neurons with powerful processing capabilities. However, little is known about the role of neuronal dendrites in behaviourally related circuit computations. Here we report that a novel global dendritic nonlinearity is involved in the integration of sensory and motor information within layer 5 pyramidal neurons during an active sensing behaviour. Layer 5 pyramidal neurons possess elaborate dendritic arborizations that receive functionally distinct inputs, each targeted to spatially separate regions1,2. At the cellular level, coincident input from these segregated pathways initiates regenerative dendritic electrical events that produce bursts of action potential output3,4 and circuits featuring this powerful dendritic nonlinearity can implement computations based on input correlation5. To examine this in vivo we recorded dendritic activity in layer 5 pyramidal neurons in the barrel cortex using two-photon calcium imaging in mice performing an object-localization task. Large-amplitude, global calcium signals were observed throughout the apical tuft dendrites when active touch occurred at particular object locations or whisker angles. Such global calcium signals are produced by dendritic plateau potentials that require both vibrissal sensory input and primary motor cortex activity. These data provide direct evidence of nonlinear dendritic processing of correlated sensory and motor information in the mammalian neocortex during active sensation.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Active touch evoked large dendritic Ca 2+ signals in distal tuft branches of layer 5 pyramids.
Figure 2: Global dendritic tuft Ca 2+ signals during active sensing.
Figure 3: Dendritic Ca 2+ signals are produced by tuft plateau potentials.
Figure 4: vM1 silencing abolished touch-evoked dendritic responses.
Figure 5: Whisker-angle dependence and object-location selectivity.

Similar content being viewed by others

References

  1. Cauller, L. J., Clancy, B. & Connors, B. W. Backward cortical projections to primary somatosensory cortex in rats extend long horizontal axons in layer I. J. Comp. Neurol. 390, 297–310 (1998)

    Article  CAS  Google Scholar 

  2. Petreanu, L., Mao, T., Sternson, S. M. & Svoboda, K. The subcellular organization of neocortical excitatory connections. Nature 457, 1142–1145 (2009)

    Article  ADS  CAS  Google Scholar 

  3. Larkum, M. E., Zhu, J. J. & Sakmann, B. A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398, 338–341 (1999)

    Article  ADS  CAS  Google Scholar 

  4. Williams, S. R. & Stuart, G. J. Dependence of EPSP efficacy on synapse location in neocortical pyramidal neurons. Science 295, 1907–1910 (2002)

    Article  ADS  CAS  Google Scholar 

  5. Takahashi, H. & Magee, J. C. Pathway interactions and synaptic plasticity in the dendritic tuft regions of CA1 pyramidal neurons. Neuron 62, 102–111 (2009)

    Article  CAS  Google Scholar 

  6. Spruston, N. Pyramidal neurons: dendritic structure and synaptic integration. Nature Rev. Neurosci. 9, 206–221 (2008)

    Article  CAS  Google Scholar 

  7. Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neurosci. 2, 79–87 (1999)

    Article  CAS  Google Scholar 

  8. Engel, A. K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nature Rev. Neurosci. 2, 704–716 (2001)

    Article  CAS  Google Scholar 

  9. Shadmehr, R., Smith, M. A. & Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010)

    Article  CAS  Google Scholar 

  10. Sommer, M. A. & Wurtz, R. H. Brain circuits for the internal monitoring of movements. Annu. Rev. Neurosci. 31, 317–338 (2008)

    Article  CAS  Google Scholar 

  11. Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nature Methods 6, 875–881 (2009)

    Article  CAS  Google Scholar 

  12. O'Connor, D. H. et al. Vibrissa-based object localization in head-fixed mice. J. Neurosci. 30, 1947–1967 (2010)

    Article  CAS  Google Scholar 

  13. Clack, N. G. et al. Automated tracking of whiskers in videos of head fixed rodents. PLOS Comput. Biol. 8, e1002591 (2012)

    Article  CAS  Google Scholar 

  14. de Kock, C. P. J. & 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, 16446–16450 (2009)

    Article  ADS  CAS  Google Scholar 

  15. Curtis, J. C. & Kleinfeld, D. Phase-to-rate transformations encode touch in cortical neurons of a scanning sensorimotor system. Nature Neurosci. 12, 492–501 (2009)

    Article  CAS  Google Scholar 

  16. O'Connor, D. H., Peron, S. P., Huber, D. & Svoboda, K. Neural activity in barrel cortex underlying vibrissa-based object localization in mice. Neuron 67, 1048–1061 (2010)

    Article  CAS  Google Scholar 

  17. Helmchen, F., Svoboda, K., Denk, W. & Tank, D. W. In vivo dendritic calcium dynamics in deep-layer cortical pyramidal neurons. Nature Neurosci. 2, 989–996 (1999)

    Article  CAS  Google Scholar 

  18. Jia, H., Rochefort, N. L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 1307–1312 (2010)

    Article  ADS  CAS  Google Scholar 

  19. Chen, X., Leischner, U., Rochefort, N. L., Nelken, I. & Konnerth, A. Functional mapping of single spines in cortical neurons in vivo. Nature (2011)

  20. Varga, Z., Jia, H., Sakmann, B. & Konnerth, A. Dendritic coding of multiple sensory inputs in single cortical neurons in vivo. Proc. Natl Acad. Sci. USA 108, 15420–15425 (2011)

    Article  ADS  CAS  Google Scholar 

  21. Gentet, L. J. et al. Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex. Nature Neurosci. 15, 607–612 (2012)

    Article  CAS  Google Scholar 

  22. Larkum, M. E., Nevian, T., Sandler, M., Polsky, A. & Schiller, J. Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325, 756–760 (2009)

    Article  ADS  CAS  Google Scholar 

  23. Williams, S. R. Encoding and decoding of dendritic excitation during active states in pyramidal neurons. J. Neurosci. 25, 5894–5902 (2005)

    Article  CAS  Google Scholar 

  24. Mao, T. et al. Long-range neuronal circuits underlying the interaction between sensory and motor cortex. Neuron 72, 111–123 (2011)

    Article  CAS  Google Scholar 

  25. Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012)

    Article  ADS  CAS  Google Scholar 

  26. Petreanu, L. et al. Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature (2012)

  27. Hill, D. N., Curtis, J. C., Moore, J. D. & Kleinfeld, D. Primary motor cortex reports efferent control of vibrissa motion on multiple timescales. Neuron 72, 344–356 (2011)

    Article  CAS  Google Scholar 

  28. Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009)

    Article  ADS  CAS  Google Scholar 

  29. Kleinfeld, D., Berg, R. W. & O'Connor, S. M. Anatomical loops and their electrical dynamics in relation to whisking by rat. Somatosens. Mot. Res. 16, 69–88 (1999)

    Article  CAS  Google Scholar 

  30. Dave, A. S. & Margoliash, D. Song replay during sleep and computational rules for sensorimotor vocal learning. Science 290, 812–816 (2000)

    Article  ADS  CAS  Google Scholar 

  31. Komiyama, T. et al. Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464, 1182–1186 (2010)

    Article  ADS  CAS  Google Scholar 

  32. Pologruto, T. A., Sabatini, B. L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003)

    Article  Google Scholar 

  33. Iyer, V. et al. ScanImage for in vivo laser scanning microscopy Program No. 485.2. 2009 Neuroscience Meeting Planner (Society for Neuroscience, 2009)

  34. Guizar-Sicairos, M., Thurman, S. T. & Fienup, J. R. Efficient subpixel image registration algorithms. Opt. Lett. 33, 156–158 (2008)

    Article  ADS  Google Scholar 

  35. Greenberg, D. S. & Kerr, J. N. D. Automated correction of fast motion artifacts for two-photon imaging of awake animals. J. Neurosci. Methods 176, 1–15 (2009)

    Article  Google Scholar 

  36. Mukamel, E. A., Nimmerjahn, A. & Schnitzer, M. J. Automated analysis of cellular signals from large-scale calcium imaging data. Neuron 63, 747–760 (2009)

    Article  CAS  Google Scholar 

  37. Hyvärinen, A. & Oja, E. Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)

    Article  Google Scholar 

  38. Zhao, T. et al. Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics 9, 247–261 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

We thank L. Tian and L. Looger for GCaMP3 constructs; J. Chandrashekar, N. Ryba and C. Zuker for GCaMP3 transgenic mice; W. Denk for comments on the manuscript; N. Clack, G. Myers, T. Zhao, V. Iyer, S. Peron and S. Drukmann for help with software and analysis; and L. Petreanu for help with experimental apparatus. S.R.W. is supported by the Australian research council (FT100100502) and Australian National Health and Medical Research Council (APP1004575).

Author information

Author notes

  1. Daniel Huber & Daniel H. O’Connor

    Present address: Present addresses: Department of Basic Neurosciences, University of Geneva, CH-1211 Geneva, Switzerland (D.H.); Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA (D.H.O.).,

Authors and Affiliations

  1. Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, 20147, Virginia, USA

    Ning-long Xu, Mark T. Harnett, Daniel Huber, Daniel H. O’Connor, Karel Svoboda & Jeffrey C. Magee

  2. Queensland Brain Institute, The University of Queensland, St Lucia, 4072, Queensland, Australia

    Stephen R. Williams

Authors
  1. Ning-long Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark T. Harnett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stephen R. Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel Huber
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel H. O’Connor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Karel Svoboda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeffrey C. Magee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.-L.X., K.S. and J.C.M. conceived the project and designed the experiments. N.-L.X. performed all behavioural and chronic imaging experiments, and data analysis. M.T.H. and S.R.W. carried out all in vitro experiments. J.C.M. performed in vivo dendritic recording experiments. D.H., D.H.O. and K.S. designed behavioural apparatus and whisker data-analysis code. N.-L.X. and J.C.M. wrote the paper with comments from all authors.

Corresponding author

Correspondence to Jeffrey C. Magee.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

This file contains Supplementary Figures 1-12. (PDF 4265 kb)

Active touch evoked global dendritic calcium signals

This video shows synchronized two-photon image frames (left) and high-speed whisker video tracking frames of whisker trajectory (right). Dendritic ROIs belonging to the same neuron were outlined and overlaid on the two-photon images. The pole position is indicated by the gray circle on the right panel, the color changing to red indicates when the pole rose into the whisker plane. (MOV 13466 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, Nl., Harnett, M., Williams, S. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012). https://doi.org/10.1038/nature11601

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature11601

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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