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:

Flexible information routing by transient synchrony

Nature Neuroscience volume 20, pages 1014–1022 (2017)Cite this article

Subjects

Abstract

Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in vivo, however, is hard to reconcile with such a function. Here we show that models of cortical circuits near the onset of oscillatory synchrony selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity.

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: The transient synchrony regime.
Figure 2: Co-emergence and frequency tracking of gamma bursts.
Figure 3: Transient phase-locking.
Figure 4: Information transfer during transient burst events.
Figure 5: Flexible routing of input signals through a hierarchy of areas.
Figure 6: Steering information transfer.

Similar content being viewed by others

References

  1. Olshausen, B.A., Anderson, C.H. & Van Essen, D.C. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J. Neurosci. 13, 4700–4719 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vogels, T.P. & Abbott, L.F. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat. Neurosci. 12, 483–491 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zylberberg, A., Fernández Slezak, D., Roelfsema, P.R., Dehaene, S. & Sigman, M. The brain's router: a cortical network model of serial processing in the primate brain. PLoS Comput. Biol. 6, e1000765 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Abeles, M., Hayon, G. & Lehmann, D. Modeling compositionality by dynamic binding of synfire chains. J. Comput. Neurosci. 17, 179–201 (2004).

    Article  PubMed  Google Scholar 

  5. Kumar, A., Rotter, S. & Aertsen, A. Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J. Neurosci. 28, 5268–5280 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hahn, G., Bujan, A.F., Frégnac, Y., Aertsen, A. & Kumar, A. Communication through resonance in spiking neuronal networks. PLoS Comput. Biol. 10, e1003811–e1003816 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Akam, T. & Kullmann, D.M. Oscillations and filtering networks support flexible routing of information. Neuron 67, 308–320 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Harnack, D., Ernst, U.A. & Pawelzik, K.R. A model for attentional information routing through coherence predicts biased competition and multistable perception. J. Neurophysiol. 114, 1593–1605 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fries, P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005).

    Article  PubMed  Google Scholar 

  10. Gregoriou, G.G., Gotts, S.J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Grothe, I., Neitzel, S.D., Mandon, S. & Kreiter, A.K. Switching neuronal inputs by differential modulations of gamma-band phase-coherence. J. Neurosci. 32, 16172–16180 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Burns, S.P., Xing, D. & Shapley, R.M. Is gamma-band activity in the local field potential of V1 cortex a “clock” or filtered noise? J. Neurosci. 31, 9658–9664 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Xing, D. et al. Stochastic generation of gamma-band activity in primary visual cortex of awake and anesthetized monkeys. J. Neurosci. 32, 13873–13880a (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jia, X., Tanabe, S. & Kohn, A. γ and the coordination of spiking activity in early visual cortex. Neuron 77, 762–774 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ray, S. & Maunsell, J.H.R. Do gamma oscillations play a role in cerebral cortex? Trends Cogn. Sci. 19, 78–85 (2015).

    Article  PubMed  Google Scholar 

  16. Ray, S. & Maunsell, J.H.R. Differences in gamma frequencies across visual cortex restrict their possible use in computation. Neuron 67, 885–896 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jia, X., Xing, D. & Kohn, A. No consistent relationship between gamma power and peak frequency in macaque primary visual cortex. J. Neurosci. 33, 17–25 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Okun, M. et al. Diverse coupling of neurons to populations in sensory cortex. Nature 521, 511–515 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Brunel, N. & Hakim, V. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11, 1621–1671 (1999).

    Article  CAS  PubMed  Google Scholar 

  20. Brunel, N. & Wang, X.-J. What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. J. Neurophysiol. 90, 415–430 (2003).

    Article  PubMed  Google Scholar 

  21. Bartos, M., Vida, I. & Jonas, P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat. Rev. Neurosci. 8, 45–56 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Roberts, M.J. et al. Robust gamma coherence between macaque V1 and V2 by dynamic frequency matching. Neuron 78, 523–536 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Bastos, A.M., Vezoli, J. & Fries, P. Communication through coherence with inter-areal delays. Curr. Opin. Neurobiol. 31, 173–180 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Chakrabarti, S., Martinez-Vazquez, P. & Gail, A. Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex. J. Neurophysiol. 112, 3138–3153 (2014).

    Article  PubMed  Google Scholar 

  25. Buschman, T.J. & Miller, E.K. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Schreiber, T. Measuring information transfer. Phys. Rev. Lett. 85, 461–464 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Wibral, M., Vicente, R. & Lizier, J.T. Directed Information Measures in Neuroscience (Springer, 2014).

  28. Bosman, C.A. et al. Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron 75, 875–888 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Grothe, I. et al. Attention selectively gates afferent signal transmission to area V4. Preprint at https://doi.org/10.1101/019547 (2015).

  30. Somers, D. & Kopell, N. Rapid synchronization through fast threshold modulation. Biol. Cybern. 68, 393–407 (1993).

    Article  CAS  PubMed  Google Scholar 

  31. Mazzoni, A., Panzeri, S., Logothetis, N.K. & Brunel, N. Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Comput. Biol. 4, e1000239 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Kreiter, A.K. & Singer, W. Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey. J. Neurosci. 16, 2381–2396 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ecker, A.S. et al. Decorrelated neuronal firing in cortical microcircuits. Science 327, 584–587 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Canolty, R.T. et al. Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies. Proc. Natl. Acad. Sci. USA 107, 17356–17361 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Witt, A. et al. Controlling the oscillation phase through precisely timed closed-loop optogenetic stimulation: a computational study. Front. Neural Circuits 7, 49 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tiesinga, P.H. & Sejnowski, T.J. Mechanisms for phase shifting in cortical networks and their role in communication through coherence. Front. Hum. Neurosci. 4, 196 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Battaglia, D., Brunel, N. & Hansel, D. Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation. Phys. Rev. Lett. 99, 238106 (2007).

    Article  PubMed  CAS  Google Scholar 

  38. Burkhalter, A. Many specialists for suppressing cortical excitation. Front. Neurosci. 2, 155–167 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Battaglia, D., Witt, A., Wolf, F. & Geisel, T. Dynamic effective connectivity of inter-areal brain circuits. PLoS Comput. Biol. 8, e1002438 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Dotson, N.M., Salazar, R.F. & Gray, C.M. Frontoparietal correlation dynamics reveal interplay between integration and segregation during visual working memory. J. Neurosci. 34, 13600–13613 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Osborne, L.C., Palmer, S.E., Lisberger, S.G. & Bialek, W. The neural basis for combinatorial coding in a cortical population response. J. Neurosci. 28, 13522–13531 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Harris, K.D., Csicsvari, J., Hirase, H., Dragoi, G. & Buzsáki, G. Organization of cell assemblies in the hippocampus. Nature 424, 552–556 (2003).

    Article  CAS  PubMed  Google Scholar 

  44. Buzsáki, G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68, 362–385 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Salazar, R.F., Dotson, N.M., Bressler, S.L. & Gray, C.M. Content-specific fronto-parietal synchronization during visual working memory. Science 338, 1097–1100 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lisman, J. The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme. Hippocampus 15, 913–922 (2005).

    Article  PubMed  Google Scholar 

  47. Colgin, L.L. et al. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357 (2009).

    Article  CAS  PubMed  Google Scholar 

  48. Cannon, J. et al. Neurosystems: brain rhythms and cognitive processing. Eur. J. Neurosci. 39, 705–719 (2014).

    Article  PubMed  Google Scholar 

  49. Hipp, J.F., Hawellek, D.J., Corbetta, M., Siegel, M. & Engel, A.K. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15, 884–890 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Bastos, A.M. et al. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85, 390–401 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Wang, X.J. & Buzsáki, G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J. Neurosci. 16, 6402–6413 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Golomb, D. & Rinzel, J. Clustering in globally coupled inhibitory neurons. Physica D 72, 259–282 (1994).

    Article  Google Scholar 

  53. Golomb, D. & Hansel, D. The number of synaptic inputs and the synchrony of large, sparse neuronal networks. Neural Comput. 12, 1095–1139 (2000).

    Article  CAS  PubMed  Google Scholar 

  54. Vicente, R., Wibral, M., Lindner, M. & Pipa, G. Transfer entropy--a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 45–67 (2011).

    Article  PubMed  Google Scholar 

  55. Wibral, M. et al. Measuring information-transfer delays. PLoS One 8, e55809 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gourévitch, B. & Eggermont, J.J. Evaluating information transfer between auditory cortical neurons. J. Neurophysiol. 97, 2533–2543 (2007).

    Article  PubMed  Google Scholar 

  57. Honey, C.J., Kötter, R., Breakspear, M. & Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA 104, 10240–10245 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Lungarella, M., Pitti, A. & Kuniyoshi, Y. Information transfer at multiple scales. Phys. Rev. E 76, 056117 (2007).

    Article  CAS  Google Scholar 

  59. Garofalo, M., Nieus, T., Massobrio, P. & Martinoia, S. Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PLoS One 4, e6482 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Ito, S. et al. Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PLoS One 6, e27431 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Stetter, O., Battaglia, D., Soriano, J. & Geisel, T. Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Comput. Biol. 8, e1002653 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Orlandi, J.G., Stetter, O., Soriano, J., Geisel, T. & Battaglia, D. Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging. PLoS One 9, e98842 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Vakorin, V.A., Krakovska, O.A. & McIntosh, A.R. Confounding effects of indirect connections on causality estimation. J. Neurosci. Methods 184, 152–160 (2009).

    Article  PubMed  Google Scholar 

  64. Strong, S., Koberle, R., de Ruyter van Steveninck, R. & Bialek, W. Entropy and information in neural spike trains. Phys. Rev. Lett. 80, 197 (1998).

    Article  CAS  Google Scholar 

  65. Cover, T.M. & Thomas, J.A. Elements of Information Theory 2nd edn. (Wiley-Interscience, 2006).

  66. Frenzel, S. & Pompe, B. Partial mutual information for coupling analysis of multivariate time series. Phys. Rev. Lett. 99, 204101 (2007).

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

We thank U. Ernst, C.M. Gray, A. Kreiter, K. Pawelzik and R. Shapley for discussions. This work was partially supported by the Federal Ministry for Education and Research (BMBF) under grant no. 01GQ1005B (to A.P., T.G., F.W. and D.B.), by a GGNB Excellence Stipend of the University of Göttingen (to A.P.), through CRC 889 by the Deutsche Forschungsgemeinschaft and by the VolkswagenStiftung under grant no. ZN2632 (to F.W.), and by the FP7 Marie Curie career development fellowship IEF 330792 (DynViB) (to D.B.).

Author information

Author notes

  1. Fred Wolf and Demian Battaglia: These authors contributed equally to this paper.

Authors and Affiliations

  1. Max Planck Institute for Dynamics and Self-organization, Göttingen, Germany

    Agostina Palmigiano, Theo Geisel & Fred Wolf

  2. Bernstein Center for Computational Neuroscience, Göttingen, Germany

    Agostina Palmigiano, Theo Geisel, Fred Wolf & Demian Battaglia

  3. Institute for Nonlinear Dynamics, Georg-August University School of Science, Göttingen, Germany

    Agostina Palmigiano, Theo Geisel & Fred Wolf

  4. SFB-889 Cellular Mechanisms of Sensory Processing, Göttingen, Germany

    Agostina Palmigiano & Fred Wolf

  5. Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes, Marseille, France

    Demian Battaglia

Authors
  1. Agostina Palmigiano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Theo Geisel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fred Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Demian Battaglia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.P. performed the simulations of the models and analyzed the results; A.P., F.W. and D.B. conceived the study, designed models and developed analysis pipelines; A.P., T.G., F.W. and D.B. wrote the paper. All authors discussed the results and implications.

Corresponding authors

Correspondence to Agostina Palmigiano or Demian Battaglia.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palmigiano, A., Geisel, T., Wolf, F. et al. Flexible information routing by transient synchrony. Nat Neurosci 20, 1014–1022 (2017). https://doi.org/10.1038/nn.4569

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4569

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