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.

Functional specificity of local synaptic connections in neocortical networks

Abstract

Neuronal connectivity is fundamental to information processing in the brain. Therefore, understanding the mechanisms of sensory processing requires uncovering how connection patterns between neurons relate to their function. On a coarse scale, long-range projections can preferentially link cortical regions with similar responses to sensory stimuli1,2,3,4. But on the local scale, where dendrites and axons overlap substantially, the functional specificity of connections remains unknown. Here we determine synaptic connectivity between nearby layer 2/3 pyramidal neurons in vitro, the response properties of which were first characterized in mouse visual cortex in vivo. We found that connection probability was related to the similarity of visually driven neuronal activity. Neurons with the same preference for oriented stimuli connected at twice the rate of neurons with orthogonal orientation preferences. Neurons responding similarly to naturalistic stimuli formed connections at much higher rates than those with uncorrelated responses. Bidirectional synaptic connections were found more frequently between neuronal pairs with strongly correlated visual responses. Our results reveal the degree of functional specificity of local synaptic connections in the visual cortex, and point to the existence of fine-scale subnetworks dedicated to processing related sensory information.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Imaging functional properties of neurons in vivo and indentifying the same neurons in vitro.
Figure 2: Relating orientation and direction preference to connection probability among L2/3 pyramidal neurons.
Figure 3: Relationship between response correlation to natural movies and connection probability.
Figure 4: Relationship between similarity of visual responses and probability of finding unidirectionally and bidirectionally connected pairs.

References

  1. 1

    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)

    CAS  Article  Google Scholar 

  2. 2

    Gilbert, C. D. & Wiesel, T. N. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci. 9, 2432–2442 (1989)

    CAS  Article  Google Scholar 

  3. 3

    Roerig, B. & Kao, J. P. Organization of intracortical circuits in relation to direction preference maps in ferret visual cortex. J. Neurosci. 19, RC44 (1999)

    CAS  Article  Google Scholar 

  4. 4

    Weliky, M., Kandler, K., Fitzpatrick, D. & Katz, L. C. Patterns of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns. Neuron 15, 541–552 (1995)

    CAS  Article  Google Scholar 

  5. 5

    Song, S., Sjöström, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3, e68 (2005)

    Article  Google Scholar 

  6. 6

    Yoshimura, Y., Dantzker, J. L. M. & Callaway, E. M. Excitatory cortical neurons form fine-scale functional networks. Nature 433, 868–873 (2005)

    ADS  CAS  Article  Google Scholar 

  7. 7

    Yoshimura, Y. & Callaway, E. M. Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity. Nature Neurosci. 8, 1552–1559 (2005)

    CAS  Article  Google Scholar 

  8. 8

    Brown, S. P. & Hestrin, S. Intracortical circuits of pyramidal neurons reflect their long-range axonal targets. Nature 457, 1133–1136 (2009)

    ADS  CAS  Article  Google Scholar 

  9. 9

    Thomson, A. M., Bannister, A. P., Mercer, A. & Morris, O. T. Target and temporal pattern selection at neocortical synapses. Phil. Trans. R. Soc. Lond. B 357, 1781–1791 (2002)

    Article  Google Scholar 

  10. 10

    Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. (Lond.) 551, 139–153 (2003)

    CAS  Article  Google Scholar 

  11. 11

    Mrsic-Flogel, T. D. et al. Homeostatic regulation of eye-specific responses in visual cortex during ocular dominance plasticity. Neuron 54, 961–972 (2007)

    CAS  Article  Google Scholar 

  12. 12

    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)

    ADS  CAS  Article  Google Scholar 

  13. 13

    Nimmerjahn, A., Kirchhoff, F., Kerr, J. N. D. & Helmchen, F. Sulforhodamine 101 as a specific marker of astroglia in the neocortex in vivo . Nature Methods 1, 31–37 (2004)

    CAS  Article  Google Scholar 

  14. 14

    Denk, W., Strickler, J. H. & Webb, W. W. Two-photon laser scanning fluorescence microscopy. Science 248, 73–76 (1990)

    ADS  CAS  Article  Google Scholar 

  15. 15

    Stosiek, C., Garaschuk, O., Holthoff, K. & Konnerth, A. In vivo two-photon calcium imaging of neuronal networks. Proc. Natl Acad. Sci. USA 100, 7319–7324 (2003)

    ADS  CAS  Article  Google Scholar 

  16. 16

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

    ADS  CAS  Article  Google Scholar 

  17. 17

    Alonso, J. M. & Martinez, L. M. Functional connectivity between simple cells and complex cells in cat striate cortex. Nature Neurosci. 1, 395–403 (1998)

    CAS  Article  Google Scholar 

  18. 18

    Kohn, A. & Smith, M. A. Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J. Neurosci. 25, 3661–3673 (2005)

    CAS  Article  Google Scholar 

  19. 19

    Aertsen, A. M., Gerstein, G. L., Habib, M. K. & Palm, G. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61, 900–917 (1989)

    CAS  Article  Google Scholar 

  20. 20

    Mariño, J. et al. Invariant computations in local cortical networks with balanced excitation and inhibition. Nature Neurosci. 8, 194–201 (2005)

    ADS  Article  Google Scholar 

  21. 21

    Ben-Yishai, R., Bar-Or, R. L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995)

    ADS  CAS  Article  Google Scholar 

  22. 22

    Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. & Suarez, H. H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995)

    ADS  CAS  Article  Google Scholar 

  23. 23

    Li, W., Piëch, V. & Gilbert, C. D. Contour saliency in primary visual cortex. Neuron 50, 951–962 (2006)

    CAS  Article  Google Scholar 

  24. 24

    Alonso, J. M., Usrey, W. M. & Reid, R. C. Rules of connectivity between geniculate cells and simple cells in cat primary visual cortex. J. Neurosci. 21, 4002–4015 (2001)

    CAS  Article  Google Scholar 

  25. 25

    Clopath, C., Büsing, L., Vasilaki, E. & Gerstner, W. Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature Neurosci. 13, 344–352 (2010)

    CAS  Article  Google Scholar 

  26. 26

    Tolhurst, D. J., Movshon, J. A. & Dean, A. F. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775–785 (1983)

    CAS  Article  Google Scholar 

  27. 27

    Barlow, H. Redundancy reduction revisited. Network 12, 241–253 (2001)

    CAS  Article  Google Scholar 

  28. 28

    Alitto, H. J. & Dan, Y. Function of inhibition in visual cortical processing. Curr. Opin. Neurobiol. 20, 340–346 (2010)

    CAS  Article  Google Scholar 

  29. 29

    Marshel, J. H., Mori, T., Nielsen, K. J. & Callaway, E. M. Targeting single neuronal networks for gene expression and cell labeling in vivo . Neuron 67, 562–574 (2010)

    CAS  Article  Google Scholar 

  30. 30

    Denk, W. & Horstmann, H. Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biol. 2, e329 (2004)

    Article  Google Scholar 

  31. 31

    Smith, S. L. & Trachtenberg, J. T. Experience-dependent binocular competition in the visual cortex begins at eye opening. Nature Neurosci. 10, 370–375 (2007)

    CAS  Article  Google Scholar 

  32. 32

    Rochefort, N. L. et al. Sparsification of neuronal activity in the visual cortex at eye-opening. Proc. Natl Acad. Sci. USA 106, 15049–15054 (2009)

    ADS  CAS  Article  Google Scholar 

  33. 33

    Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997)

    CAS  Article  Google Scholar 

  34. 34

    Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997)

    CAS  Article  Google Scholar 

  35. 35

    Vogelstein, J. T. et al. Fast non-negative deconvolution for spike train inference from population calcium imaging. J. Neurophysiol. (2010)

  36. 36

    Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32, 1149–1164 (2001)

    Article  Google Scholar 

  37. 37

    Debanne, D. et al. Paired-recordings from synaptically coupled cortical and hippocampal neurons in acute and cultured brain slices. Nature Protocols 3, 1559–1568 (2008)

    CAS  Article  Google Scholar 

  38. 38

    Agresti, A. Categorical Data Analysis 2nd edn (Wiley InterScience, 2002)

    Google Scholar 

Download references

Acknowledgements

We thank T. Margrie for discussions about the project and the manuscript, and J. Vogelstein for the spike inference algorithm. This work was supported by the Wellcome Trust (T.D.M.-F.), the European Research Council (T.D.M.-F.), the European Molecular Biology Organisation (S.B.H.), the Medical Research Council and FP7 grant #243914 (K.A.B., P.J.S.), the Overseas Research Students Award Scheme and UCL studentship (H.K.).

Author information

Affiliations

Authors

Contributions

H.K. and S.B.H. performed experiments and data analysis. H.K. developed image registration software using preliminary data obtained by S.B.H. and K.A.B., and programs for data analysis. B.P. developed image acquisition software and the program for extracting calcium transients. P.J.S. designed electrophysiology setup and software for acquisition and analysis. B.P., H.K., S.B.H. and T.D.M.-F. built experimental setups. H.K. and T.D.M.-F. wrote the paper.

Corresponding author

Correspondence to Thomas D. Mrsic-Flogel.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Figures

The file contains Supplementary Figures 1-6 with legends. (PDF 497 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ko, H., Hofer, S., Pichler, B. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011). https://doi.org/10.1038/nature09880

Download citation

Further reading

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

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