Skip to main content

Oscillation induced propagation of synchrony in structured neural networks

Spatio-temporally coordinated patterns of spiking activity have been experimentally observed in a range of neural circuits, but their dynamical origin is still not well understood. A prominent hypothesis states that propagating synchronized activity through embedded feed-forward networks might dynamically generate such patterns [1, 2]. Modeling studies indeed showed that such "synfire-chains" embedded in random recurrent networks enable reliable signal transmission by propagating localized (sub-network) synchrony, if their structure is strongly pronounced compared to the embedding network. This requires in particular a dense connectivity between the neuronal layers of the chain or strongly enhanced synapses and modified response properties of neurons within the chain [3]. So far, however, such prominent large-scale structures have not been experimentally observed.

Single neuron experiments [4] indicate that neuronal dendrites are capable of nonlinearly amplifying sufficiently synchronous inputs by eliciting dendritic spikes, thereby inducing non-additive interactions. Here we demonstrate that such non-additive coupling promotes guided synchrony propagation already in random recurrent neural networks with mildly enhanced, biologically plausible sub-structures and without anatomically superimposed feed-forward chains [5]. Our analysis explains the mechanisms underlying robust propagation and shows in which sense non-additive enhancement -- a local neuron property that dynamically changes with input synchrony -- may complement dense and non-local structural connectivity.

Most neuronal circuits exhibit oscillations of various frequencies and amplitudes [6]. Such oscillations may influence the dynamics of synchrony propagation. We thus further study this influence for both externally induced oscillations as well as for oscillations generated by the rhythmically propagating synchronous activity itself. We find that in networks with linear dendrites and balanced input, the oscillations hinder synchrony propagation, if they effect the dynamics at all. In contrast, for non-additive coupling, oscillations support synchronous propagation, if they are in resonance and the interplay between oscillations and propagating activity induces complex locking patterns: We show that in recurrent circuits containing high-connectivity (hub-)neurons, the oscillatory echo to propagating synchrony can generate synchrony in the remainder of the network and thereby in turn stabilize or even enable synchrony propagation along predefined paths: The network echo promotes signal transmission.


  1. 1.

    Abeles M: Corticonics: Neuronal Circuits of the Cerebral Cortex. 1991, Cambridge University Press

    Google Scholar 

  2. 2.

    Diesmann M, Gewaltig M-O, Aertsen A: Conditions for Stable Propagation of Synchronous Spiking in Cortical Neural Networks. Nature. 1999, 402: 529-10.1038/990101.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Kumar A, Rotter S, Aertsen A: Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model. J Neurosc. 2008, 28: 5268-10.1523/JNEUROSCI.2542-07.2008.

    CAS  Article  Google Scholar 

  4. 4.

    Vogels TP, Abbott LF: Signal Propagation in Networks of Integrate-and-Fire Neurons. J Neurosci. 2005, 25: 10786-10.1523/JNEUROSCI.3508-05.2005.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Ariav G, Polsky A, Schiller J: Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA1 pyramidal neurons. J Neurosci. 2003, 23: 7750-

    CAS  PubMed  Google Scholar 

  6. 6.

    Gasparini S, Magee J: State dependent dendritic computation in hippocampal CA1 pyramidal neurons. J Neurosci. 2006, 26: 2088-10.1523/JNEUROSCI.4428-05.2006.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Losonczy A, Makara J, Magee J: Compartmentalized dendritic plasticity and input feature storage in neurons. Nature. 2008, 452: 4361-

    Article  Google Scholar 

  8. 8.

    Jahnke S, Timme M, Memmesheimer R-M: Guiding Synchrony through Random Networks. Phys Rev X. 2012, 2: 041016-

    CAS  Google Scholar 

  9. 9.

    Memmesheimer R-M, Timme M: Non-Additive Coupling Enables Propagation of Synchronous Spiking Activity in Purely Random Networks. PLoS Comput Biol. 2012, 8: e1002384-10.1371/journal.pcbi.1002384.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  10. 10.

    Buzsáki G, Draguhn A: Neuronal Oscillations in Cortical Networks. Science. 2004, 304: 1926-10.1126/science.1099745.

    Article  PubMed  Google Scholar 

Download references


We acknowledge support by the BMBF (Grant No. 01GQ1005B) and the DFG (Grant No. TI 629/3-1).

Author information



Corresponding author

Correspondence to Sven Jahnke.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Jahnke, S., Memmesheimer, R. & Timme, M. Oscillation induced propagation of synchrony in structured neural networks. BMC Neurosci 14, P390 (2013).

Download citation


  • Recurrent Neural Network
  • Neuronal Layer
  • Synchrony Propagation
  • Synchronous Propagation
  • Structure Neural Network