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Dynamics of excitable neural networks with heterogeneous connectivity

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons75278

Besserve,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chavez, M., Besserve, M., & Le Van Quyen, M. (2011). Dynamics of excitable neural networks with heterogeneous connectivity. Progress in Biophysics and Molecular Biology, 105(1-2), 29-33. doi:10.1016/j.pbiomolbio.2010.11.002.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BC58-F
Abstract
A central issue of neuroscience is to understand how neural units integrates internal and external signals to create coherent states. Recently, it has been shown that the sensitivity and dynamic range of neural assemblies are optimal at a critical coupling among its elements. Complex architectures of connections seem to play a constructive role on the reliable coordination of neural units. Here we show that, the synchronizability and sensitivity of excitable neural networks can be tuned by diversity in the connections strengths. We illustrate our findings for weighted networks with regular, random and complex topologies. Additional comparisons of real brain networks support previous studies suggesting that heterogeneity in the connectivity may play a constructive role on information processing. These findings provide insights into the relationship between structure and function of neural circuits.