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On the Dynamics of the Spontaneous Activity in Neuronal Networks

Author

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  • Alberto Mazzoni
  • Frédéric D Broccard
  • Elizabeth Garcia-Perez
  • Paolo Bonifazi
  • Maria Elisabetta Ruaro
  • Vincent Torre

Abstract

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics.

Suggested Citation

  • Alberto Mazzoni & Frédéric D Broccard & Elizabeth Garcia-Perez & Paolo Bonifazi & Maria Elisabetta Ruaro & Vincent Torre, 2007. "On the Dynamics of the Spontaneous Activity in Neuronal Networks," PLOS ONE, Public Library of Science, vol. 2(5), pages 1-12, May.
  • Handle: RePEc:plo:pone00:0000439
    DOI: 10.1371/journal.pone.0000439
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    Cited by:

    1. Sinisa Pajevic & Dietmar Plenz, 2009. "Efficient Network Reconstruction from Dynamical Cascades Identifies Small-World Topology of Neuronal Avalanches," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-20, January.
    2. Bashkirtseva, Irina A. & Ryashko, Lev B. & Pisarchik, Alexander N., 2020. "Ring of map-based neural oscillators: From order to chaos and back," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    3. Marc Benayoun & Jack D Cowan & Wim van Drongelen & Edward Wallace, 2010. "Avalanches in a Stochastic Model of Spiking Neurons," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-13, July.
    4. Simone Orcioni & Alessandra Paffi & Francesca Apollonio & Micaela Liberti, 2020. "Revealing Spectrum Features of Stochastic Neuron Spike Trains," Mathematics, MDPI, vol. 8(6), pages 1-13, June.
    5. Guido Gigante & Gustavo Deco & Shimon Marom & Paolo Del Giudice, 2015. "Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-23, November.
    6. Bershadskii, A. & Ikegaya, Y., 2011. "Chaotic neuron clock," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 342-347.
    7. Matthias Rybarsch & Stefan Bornholdt, 2014. "Avalanches in Self-Organized Critical Neural Networks: A Minimal Model for the Neural SOC Universality Class," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-8, April.

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