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  • Open Access

Oscillation induced propagation of synchrony in structured neural networks

BMC Neuroscience201314 (Suppl 1) :P390

  • Published:


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

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.



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

Authors’ Affiliations

Network Dynamics, Max Planck Institute for Dynamics & Self-Organization, Göttingen, Germany
Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
Fakultät für Physik, Georg-August-Universität Göttingen, Göttingen, Germany
Donders Institute, Department for Neuroinformatics, Radboud University, Nijmegen, Netherlands


  1. Abeles M: Corticonics: Neuronal Circuits of the Cerebral Cortex. 1991, Cambridge University PressView ArticleGoogle Scholar
  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.View ArticlePubMedGoogle Scholar
  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.View ArticleGoogle Scholar
  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.View ArticlePubMedGoogle Scholar
  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-PubMedGoogle Scholar
  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.View ArticlePubMedGoogle Scholar
  7. Losonczy A, Makara J, Magee J: Compartmentalized dendritic plasticity and input feature storage in neurons. Nature. 2008, 452: 4361-View ArticleGoogle Scholar
  8. Jahnke S, Timme M, Memmesheimer R-M: Guiding Synchrony through Random Networks. Phys Rev X. 2012, 2: 041016-Google Scholar
  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 CentralView ArticlePubMedGoogle Scholar
  10. Buzsáki G, Draguhn A: Neuronal Oscillations in Cortical Networks. Science. 2004, 304: 1926-10.1126/science.1099745.View ArticlePubMedGoogle Scholar


© Jahnke et al; licensee BioMed Central Ltd. 2013

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.