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Portraits of communication in neuronal networks

Nature Reviews Neurosciencevolume 20pages117127 (2019) | Download Citation

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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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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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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Contributions

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.

Corresponding authors

Correspondence to Gerald Hahn or Arvind Kumar.

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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https://doi.org/10.1038/s41583-018-0094-0