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OPINION

Portraits of communication in neuronal networks

Nature Reviews Neuroscience volume 20, pages 117–127 (2019)Cite this article

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Abstract

The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

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Fig. 1: Elements of communication in neuronal networks.
Fig. 2: Neuronal communication with gamma oscillations.
Fig. 3: Communication with gamma oscillations is modulated by slower oscillations in the alpha range.
Fig. 4: Summary of neuronal communication in α–σ state space.

References

  1. Modha, D. S. & Singh, R. Network architecture of the long-distance pathways in the macaque brain. Proc. Natl Acad. Sci. USA 107, 13485–13490 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Kumar, A., Rotter, S. & Aertsen, A. Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11, 615–627 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. Dehaene, S. & Changeux, J.-P. Experimental and theoretical approaches to conscious processing. Neuron 70, 200–227 (2011).

    CAS  PubMed  Google Scholar 

  4. Dehaene, S., Sergent, C. & Changeux, J.-P. A neuronal network model linking subjective reports and objective physiological data during conscious perception. Proc. Natl Acad. Sci. USA 100, 8520–8525 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Aertsen, A. & Preissl, H. in Nonlinear Dynamics and Neuronal Networks (ed. Schuster, H.) (VCH, Weinheim, 1991).

  6. Bienenstock, E. A model of neocortex. Netw. Comput. Neural Syst. 6, 179–224 (1995).

    Google Scholar 

  7. Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat. Rev. Neurosci. 19, 17–33 (2017).

    PubMed  Google Scholar 

  8. Friston, K. J. Functional and effective connectivity: a review. Brain Connect. 1, 13–36 (2011).

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  10. Perkel, D. & Bullock, T. Neural coding: a report based on an NRP work session. Neurosci. Res. Progr. Bull. 6, 219–349 (1968).

    Google Scholar 

  11. Abeles, M. Corticonics: Neural Circuits of the Cerebral Cortex (Cambridge Univ. Press, 1991).

  12. Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci. 15, 264–278 (2014).

    PubMed  PubMed Central  Google Scholar 

  13. Markram, H. & Tsodyks, M. Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382, 807–810 (1996).

    CAS  PubMed  Google Scholar 

  14. Stevens, C. F. & Wang, Y. Facilitation and depression at single central synapses. Neuron 14, 795–802 (1995).

    CAS  PubMed  Google Scholar 

  15. Arieli, A., Sterkin, A., Grinvald, A. & Aertsen, A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996).

    CAS  PubMed  Google Scholar 

  16. Churchland, M. M. et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369–378 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Haider, B., Häusser, M. & Carandini, M. Inhibition dominates sensory responses in the awake cortex. Nature 493, 97–100 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Rudolph, M., Pospischil, M., Timofeev, I. & Destexhe, A. Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex. J. Neurosci. 27, 5280–5290 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Diesmann, M., Gewaltig, M.-O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).

    CAS  PubMed  Google Scholar 

  20. Fries, P. Rhythms for cognition: communication through coherence. Neuron 88, 220–235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity (Springer-Verlag Berlin Heidelberg, 1998).

  22. Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394 (1972).

    CAS  PubMed  Google Scholar 

  23. Adrian, E. The Basis of Sensation: the Action of the Sense Organs (Christophers Publishing, 1949).

  24. Riehle, A., Grün, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).

    CAS  PubMed  Google Scholar 

  25. Vaadia, E. et al. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373, 515–518 (1995).

    CAS  PubMed  Google Scholar 

  26. Shinomoto, S. et al. Relating neuronal firing patterns to functional differentiation of cerebral cortex. PLOS Comput. Biol. 5, e1000433 (2009).

    PubMed  PubMed Central  Google Scholar 

  27. Maimon, G. & Assad, J. A. Beyond poisson: increased spike-time regularity across primate parietal cortex. Neuron 62, 426–440 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Luczak, A., McNaughton, B. L. & Harris, K. D. Packet-based communication in the cortex. Nat. Rev. Neurosci. 16, 745–755 (2015).

    CAS  PubMed  Google Scholar 

  29. Aertsen, A., Diesmann, M. & Gewaltig, M. Propagation of synchronous spiking activity in feedforward neural networks. J. Physiol. 90, 243–247 (1996).

    CAS  Google Scholar 

  30. Grün, S. & Rotter, S. (eds) Analysis of Parallel Spike Trains (Springer US, 2010).

  31. Gewaltig, M. O., Diesmann, M. & Aertsen, A. Propagation of cortical synfire activity: survival probability in single trials and stability in the mean. Neural Netw. 14, 657–673 (2001).

    CAS  PubMed  Google Scholar 

  32. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kremkow, J., Aertsen, A. & Kumar, A. Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition. J. Neurosci. 30, 15760–15768 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Griffith, J. S. On the stability of brain-like structures. Biophys. J. 3, 299–308 (1963).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Litvak, V., Sompolinsky, H., Segev, I. & Abeles, M. On the transmission of rate code in long feedforward networks with excitatory-inhibitory balance. J. Neurosci. 23, 3006–3015 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Reyes, A. D. Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nat. Neurosci. 6, 593–599 (2003).

    CAS  PubMed  Google Scholar 

  37. Vogels, T. P. & Abbott, L. F. Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci. 25, 10786–10795 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Goedeke, S. & Diesmann, M. The mechanism of synchronization in feed-forward neuronal networks. New J. Phys. 10, 015007 (2008).

    Google Scholar 

  39. Ratté, S., Hong, S., De Schutter, E. & Prescott, S. A. Impact of neuronal properties on network coding: Roles of spike initiation dynamics and robust synchrony transfer. Neuron 78, 758–772 (2013).

    PubMed  PubMed Central  Google Scholar 

  40. Marder, E., O’Leary, T. & Shruti, S. Neuromodulation of circuits with variable parameters: single neurons and small circuits reveal principles of state-dependent and robust neuromodulation. Annu. Rev. Neurosci. 37, 329–346 (2014).

    CAS  PubMed  Google Scholar 

  41. Kuhn, A., Aertsen, A. & Rotter, S. Neuronal integration of synaptic input in the fluctuation-driven regime. J. Neurosci. 24, 2345–2356 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Sherman, S. M. Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 19, 533–541 (2016).

    CAS  PubMed  Google Scholar 

  43. Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).

    CAS  PubMed  Google Scholar 

  44. DeFelipe, J. et al. New insights into the classification and nomenclature of cortical GABAergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. Klausberger, T. & Somogyi, P. Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations. Science 321, 53–57 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Jonas, P. & Buzsaki, G. Neural inhibition. Scholarpedia 2, 3286 (2007).

    Google Scholar 

  48. Fino, E., Packer, A. M. & Yuste, R. The logic of inhibitory connectivity in the neocortex. Neuroscientist 19, 228–237 (2013).

    PubMed  Google Scholar 

  49. Brunel, N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183–208 (2000).

    CAS  PubMed  Google Scholar 

  50. Ledoux, E. & Brunel, N. Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs. Front. Comput. Neurosci. 5, 25 (2011).

    PubMed  PubMed Central  Google Scholar 

  51. Sahasranamam, A., Vlachos, I., Aertsen, A. & Kumar, A. Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity. Sci. Rep. 6, 26029 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 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).

    PubMed  Google Scholar 

  53. Bruno, R. M. & Sakmann, B. Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627 (2006).

    CAS  PubMed  Google Scholar 

  54. Castelo-Branco, M., Neuenschwander, S. & Singer, W. Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat. J. Neurosci. 18, 6395–6410 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Palmigiano, A., Geisel, T., Wolf, F. & Battaglia, D. Flexible information routing by transient synchrony. Nat. Neurosci. 20, 1014–1022 (2017).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  57. Kumar, A., Schrader, S., Aertsen, A. & Rotter, S. The high-conductance state of cortical networks. Neural Comput. 20, 1–43 (2008).

    PubMed  Google Scholar 

  58. Renart, A. et al. The asynchronous state in cortical circuits. Science 327, 587–590 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Tetzlaff, T., Helias, M., Einevoll, G. T. & Diesmann, M. Decorrelation of neural-network activity by inhibitory feedback. PLOS Comput. Biol. 8, e1002596 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Zandvakili, A. & Kohn, A. Coordinated neuronal activity enhances corticocortical communication. Neuron 87, 827–839 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Mokeichev, A. et al. Stochastic emergence of repeating cortical motifs in spontaneous membrane potential fluctuations in vivo. Neuron 53, 413–425 (2007).

    CAS  PubMed  Google Scholar 

  63. Ikegaya, Y. et al. Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004).

    CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  65. Buzsáki, G. Rhythms of the Brain (Oxford Univ. Press, 2006).

  66. Buehlmann, A. & Deco, G. Optimal information transfer in the cortex through synchronization. PLOS Comput. Biol. 6, e1000934 (2010).

    PubMed  PubMed Central  Google Scholar 

  67. Womelsdorf, T. et al. Modulation of neuronal interactions through neuronal synchronization. Science 316, 1609–1612 (2007).

    CAS  PubMed  Google Scholar 

  68. Voloh, B. & Womelsdorf, T. A role of phase-resetting in coordinating large scale neural networks during attention and goal-directed behavior. Front. Syst. Neurosci. 10, 18 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  71. Akam, T. & Kullmann, D. M. Oscillatory multiplexing of population codes for selective communication in the mammalian brain. Nat. Rev. Neurosci. 15, 111–122 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  74. Singer, W. Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65 (1999).

    CAS  PubMed  Google Scholar 

  75. Singer, W. & Gray, C. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995).

    CAS  PubMed  Google Scholar 

  76. Gray, C. M., König, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).

    CAS  PubMed  Google Scholar 

  77. Bringuier, V., Fregnac, Y., Debanne, D., Shulz, D. & Baranyi, A. Synaptic origin of rhythmic visually evoked activity in kitten area 17 neurones. Neuroreport 3, 1065–1068 (1992).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Cardin, J. A. et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459, 663–667 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Buzsáki, G. & Wang, X.-J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).

    PubMed  PubMed Central  Google Scholar 

  83. Lepousez, G. & Lledo, P.-M. Odor discrimination requires proper olfactory fast oscillations in awake mice. Neuron 80, 1010–1024 (2013).

    CAS  PubMed  Google Scholar 

  84. Vierling-Claassen, D., Cardin, J. A., Moore, C. I. & Jones, S. R. Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Front. Hum. Neurosci. 4, 198 (2010).

    PubMed  PubMed Central  Google Scholar 

  85. Mejias, J. F., Murray, J. D., Kennedy, H. & Wang, X.-J. Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Sci. Adv. 2, e1601335 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. Hyafil, A., Fontolan, L., Kabdebon, C., Gutkin, B. & Giraud, A.-L. Speech encoding by coupled cortical theta and gamma oscillations. eLife 4, e06213 (2015).

    PubMed  PubMed Central  Google Scholar 

  87. Hyafil, A., Giraud, A.-L., Fontolan, L. & Gutkin, B. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends Neurosci. 38, 725–740 (2015).

    CAS  PubMed  Google Scholar 

  88. Jensen, O. & Mazaheri, A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front. Hum. Neurosci. 4, 186 (2010).

    PubMed  PubMed Central  Google Scholar 

  89. Klimesch, W., Sauseng, P. & Hanslmayr, S. EEG alpha oscillations: the inhibition–timing hypothesis. Brain Res. Rev. 53, 63–88 (2007).

    PubMed  Google Scholar 

  90. Haegens, S., Nacher, V., Luna, R., Romo, R. & Jensen, O. Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl Acad. Sci. USA 108, 19377–19382 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Bonnefond, M., Kastner, S. & Jensen, O. Communication between brain areas based on nested oscillations. eNeuro 4, ENEURO.0153-16.2017 (2017).

    Google Scholar 

  92. Pfurtscheller, G. Induced oscillations in the alpha band: functional meaning. Epilepsia 44, 2–8 (2003).

    PubMed  Google Scholar 

  93. Gips, B., van der Eerden, J. P. & Jensen, O. A biologically plausible mechanism for neuronal coding organized by the phase of alpha oscillations. Eur. J. Neurosci. 44, 2147–2161 (2016).

    PubMed  PubMed Central  Google Scholar 

  94. Jensen, O., Gips, B., Bergmann, T. O. & Bonnefond, M. Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing. Trends Neurosci. 37, 357–369 (2014).

    CAS  PubMed  Google Scholar 

  95. Palva, S. & Palva, J. M. New vistas for α-frequency band oscillations. Trends Neurosci. 30, 150–158 (2007).

    CAS  PubMed  Google Scholar 

  96. Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134 (2012).

    CAS  PubMed  Google Scholar 

  97. Michalareas, G. et al. Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron 89, 384–397 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Fell, J. & Axmacher, N. The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 12, 105–118 (2011).

    CAS  PubMed  Google Scholar 

  99. Markram, H., Luebke, J., Frotscher, M. & Sakmann, B. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997).

    CAS  PubMed  Google Scholar 

  100. Kumar, A. & Mehta, M. R. Frequency-dependent changes in NMDAR-dependent synaptic plasticity. Front. Comput. Neurosci. 5, 38 (2011).

    PubMed  PubMed Central  Google Scholar 

  101. Buschman, T. J. & Kastner, S. From behavior to neural dynamics: an integrated theory of attention. Neuron 88, 127–144 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Buehlmann, A. & Deco, G. The neuronal basis of attention: rate versus synchronization modulation. J. Neurosci. 28, 7679–7686 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Harris, K. D. & Thiele, A. Cortical state and attention. Nat. Rev. Neurosci. 12, 509–523 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Fries, P., Reynolds, J., Rorie, A. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).

    CAS  PubMed  Google Scholar 

  105. Richter, C. G., Thompson, W. H., Bosman, C. A. & Fries, P. Top-down beta enhances bottom-up gamma. J. Neurosci. 37, 6698–6711 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Nikolic, D. Model this! Seven empirical phenomena missing in the models of cortical oscillatory dynamics. Proc. Int. Jt Conf. Neural Netw. https://doi.org/10.1109/IJCNN.2009.5179076 (2009).

  107. Bisley, J. W. & Goldberg, M. E. Attention, intention, and priority in the parietal lobe. Annu. Rev. Neurosci. 33, 1–21 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Zelinsky, G. J. & Bisley, J. W. The what, where, and why of priority maps and their interactions with visual working memory. Ann. NY Acad. Sci. 1339, 154–164 (2015).

    PubMed  Google Scholar 

  109. Deco, G. & Kringelbach, M. L. Hierarchy of information processing in the brain: a novel ‘intrinsic ignition’ framework. Neuron 94, 961–968 (2017).

    CAS  PubMed  Google Scholar 

  110. Pikovsky, A., Rosenblum, M. & Kurths, J. Synchronization: a Universal Concept in Nonlinear Sciences (Cambridge Univ. Press, 2003).

  111. Zheng, Z., Hu, G. & Hu, B. Phase slips and phase synchronization of coupled oscillators. Phys. Rev. Lett. 81, 5318–5321 (1998).

    CAS  Google Scholar 

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Acknowledgements

The authors thank S. Dehaene, M. Gilson, J. Goldman, R. Kaplan, T. van Kerkoerle, M. Kringelbach, T. Pfeffer, J. Mejias, P. Uhlhaas, E. Hugues and M. Filipovic for useful discussions and comments on earlier versions of the manuscript.

Reviewer information

Nature Reviews Neuroscience thanks S. Hanslmayr, A. Luczak, T. Womelsdorf and the other, anonymous reviewer for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Gerald Hahn, Adrian Ponce-Alvarez & Gustavo Deco

  2. Institució Catalana de la Recerca i Estudis Avançats, Universitat Pompeu Fabra, Barcelona, Spain

    Gustavo Deco

  3. Faculty of Biology, University of Freiburg, Freiburg, Germany

    Ad Aertsen

  4. Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany

    Ad Aertsen & Arvind Kumar

  5. Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

    Arvind Kumar

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G.H. and A.K. researched data for article, provided substantial contributions to the discussion of its content, wrote the article and reviewed and edited the manuscript before submission. A.A. provided a substantial contribution to the discussion of the article's content, wrote and the article and reviewed and edited and manuscript before submission. A.P.-A. and G.D. provided substantial contributions to the discussion of the article’s content and reviewed and edited the manuscript before submission.

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Supplementary Information

Glossary

Asynchronous-irregular (AI) state

An activity state in which individual neurons spike in an irregular manner, independent (asynchronous) of other neurons in the network. In this state, the irregularity of the inter-spike-interval is close to unity, and correlations between a pair of neurons are close to zero.

Convergent and divergent projections

Projections in a connectivity scheme in which neurons in a group receive input from many neurons in a previous group (convergent) and, at the same time, project to many neurons in the subsequent groups (divergent).

Communication through resonance

A mode of communication in which the non-oscillatory receiver network is periodically activated by the sender and generates an amplified oscillatory response through resonance. Once the oscillations in the receiver are strong enough, only the pulse packets aligned to the peak (or trough if the oscillation is effectively inhibitory) are transmitted to the receiver network.

Communication through coherence

A mode of communication in which both the sender and receiver oscillate with the same frequency and phase (coherent). In this model of communication, only the pulse packets aligned to the peak (or trough when the oscillations are effectively inhibitory) are transmitted to the receiver network.

Effective spike threshold

The difference between the average membrane potential and the spike threshold of a neuron.

Excitatory separatrix

A separatrix of a feedforward network consisting of only excitatory neurons.

Inhibitory separatrix

A separatrix of a feedforward network consisting of both excitatory and inhibitory neurons. As inhibition is introduced in the network, the excitatory separatrix moves upwards, indicating that in the presence of inhibition, stronger and more synchronous pulse packets are allowed to transmit.

Oscillation-based communication

When communication between the sender and receiver is mediated by communication through either resonance or coherence.

Separatrix

A line that separates the two-dimensional space spanned by the two descriptors (α and σ) of a pulse packet. An input pulse packet starting above the separatrix eventually converges to a fixed point corresponding to a high α and a low σ. By contrast, an input pulse packet starting below the separatrix eventually converges to a fixed point corresponding to a small α and a high σ.

Synchronous-irregular state

An activity state in which individual neurons spike in an irregular manner but different neurons are correlated with each other. In this state, the irregularity of the inter-spike-interval is close to unity, and correlations between a pair of neurons are non-zero.

Synfire mode of communication

This mode of communication is observed when the input pulse packet is strong and synchronous enough to be above the separatrix. Alternatively, such communication occurs when the connectivity is sufficiently dense to lower the separatrix such that even weak or asynchronous pulse packets can propagate without the need for oscillations.

Stochastic oscillation

(SO). A type of oscillation in neuronal networks in which the average activity of the neuron population shows a regular oscillation but individual neurons do not spike in each cycle and instead spike in an irregular manner.

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Hahn, G., Ponce-Alvarez, A., Deco, G. et al. Portraits of communication in neuronal networks. Nat Rev Neurosci 20, 117–127 (2019). https://doi.org/10.1038/s41583-018-0094-0

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