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BMC Neuroscience

, 13:P179 | Cite as

Synergetic role of inhibition and excitation in bursting synchronization

Open Access
Poster presentation
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Keywords

Network Topology Coupling Strength Large Network Stability Method Inhibitory Input 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We study the influence of coupling strength and network topology on synchronization in strongly coupled networks of square-wave bursters with fast excitatory and inhibitory connections. Fast excitation is known to promote synchrony, which appears in purely excitatory networks of bursting neurons as long as the excitatory coupling exceeds a threshold value. On the contrary, fast strong inhibition leads to anti-phase or asynchronous bursting in purely inhibitory networks.

In this work, we report a surprising find that the addition of strong fast inhibitory connections to excitatory networks of square-wave bursters can promote synchrony. More precisely, the inhibitory connections, introduced to an excitatory network, can lower the synchronization threshold much more significantly than strengthening the present excitatory connections.

We use the stability methods, developed in [1, 2], to explain this synergetic role of otherwise destabilizing inhibition in promoting stable synchronization of bursting neurons in excitatory networks. We demonstrate that there is a balance between the excitatory and inhibitory couplings providing the maximum stability of synchronization. These results are applicable to synchronization in a pair of mutually connected neurons as well as to large networks with mixed excitatory-inhibitory couplings. We also study the interplay between burst synchronization and the excitatory and inhibitory networks’ structures and show that the number of excitatory and inhibitory inputs each neuron receives is often the crucial quantity.

Notes

Acknowledgements

This work was supported by the National Science Foundation under Grant DMS-1009744 and the GSU Brains and Behavior program.

References

  1. 1.
    Belykh I, de Lange E, Hasler M: Synchronization of bursting neurons: what matters in the network topology. Phys Rev Lett. 2005, 94: 188101CrossRefPubMedGoogle Scholar
  2. 2.
    Belykh I, Hasler M: Mesoscale and clusters of synchrony in networks of bursting neurons. Chaos. 2011, 21: 016106-10.1063/1.3563581.CrossRefPubMedGoogle Scholar

Copyright information

© Belykh; licensee BioMed Central Ltd. 2012

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 (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Mathematics and Statistics and Neuroscience InstituteGeorgia State UniversityAtlantaUSA

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