Perspective | Published:


Portraits of communication in neuronal networks


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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 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).

  2. 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).

  3. 3.

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

  4. 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).

  5. 5.

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

  6. 6.

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

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

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

  11. 11.

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

  12. 12.

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

  13. 13.

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

  14. 14.

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

  15. 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).

  16. 16.

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

  17. 17.

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

  18. 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).

  19. 19.

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

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

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

  24. 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).

  25. 25.

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

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

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

  30. 30.

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

  31. 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).

  32. 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).

  33. 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).

  34. 34.

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

  35. 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).

  36. 36.

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

  37. 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).

  38. 38.

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

  39. 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).

  40. 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).

  41. 41.

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

  42. 42.

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

  43. 43.

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

  44. 44.

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

  45. 45.

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

  46. 46.

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

  47. 47.

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

  48. 48.

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

  49. 49.

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

  50. 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).

  51. 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).

  52. 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).

  53. 53.

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

  54. 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).

  55. 55.

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

  56. 56.

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

  57. 57.

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

  58. 58.

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

  59. 59.

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

  60. 60.

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

  61. 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).

  62. 62.

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

  63. 63.

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

  64. 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).

  65. 65.

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

  66. 66.

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

  67. 67.

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

  68. 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).

  69. 69.

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

  70. 70.

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

  71. 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).

  72. 72.

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

  73. 73.

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

  74. 74.

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

  75. 75.

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

  76. 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).

  77. 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).

  78. 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).

  79. 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).

  80. 80.

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

  81. 81.

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

  82. 82.

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

  83. 83.

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

  84. 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).

  85. 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).

  86. 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).

  87. 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).

  88. 88.

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

  89. 89.

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

  90. 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).

  91. 91.

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

  92. 92.

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

  93. 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).

  94. 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).

  95. 95.

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

  96. 96.

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

  97. 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).

  98. 98.

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

  99. 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).

  100. 100.

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

  101. 101.

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

  102. 102.

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

  103. 103.

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

  104. 104.

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

  105. 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).

  106. 106.

    Nikolic, D. Model this! Seven empirical phenomena missing in the models of cortical oscillatory dynamics. Proc. Int. Jt Conf. Neural Netw. (2009).

  107. 107.

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

  108. 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).

  109. 109.

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

  110. 110.

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

  111. 111.

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

Download references


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.

Author information

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.

Competing interests

The authors declare no competing interests.

Correspondence to Gerald Hahn or Arvind Kumar.

Supplementary Information

Supplentary Figures


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.


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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark
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 ασ state space.