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This article is part of the supplement: Twentieth Annual Computational Neuroscience Meeting: CNS*2011

Open Access Poster presentation

Interdependence between network dynamics and connectivity in dissociated cortical cultures: a theoretical and experimental approach

Paolo Massobrio1*, Valentina Pasquale1, Matteo Garofalo1 and Sergio Martinoia12

Author Affiliations

1 Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Genova, Italy

2 Department of Neuroscience and Brain Technologies, Italian Institute of Technology (IIT), Genova, Italy

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BMC Neuroscience 2011, 12(Suppl 1):P214  doi:10.1186/1471-2202-12-S1-P214


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/12/S1/P214


Published:18 July 2011

© 2011 Massobrio et al; licensee 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.

Poster presentation

In this work we aimed at investigating the interdependence between connectivity and dynamics in large-scale cortical networks cultured in vitro onto planar Micro-Electrode Arrays (MEAs). In this experimental model, neurons are free of predefined constraints and thus able to re-create networks that exhibit complex and highly variable spatio-temporal patterns of activity composed of synchronized bursts, mixed with random spikes. Starting from this experimental evidence, here we address the questions: does a particular network architecture promote such dynamics? Is it possible to predict the activity of a neuronal network on the basis of its connectivity map?

We determined the dynamic state of the network according to the statistical distribution of neuronal avalanches (namely critical, sub-critical or super-critical) [1,2]. Due to the difficulties of determining the network topology of our cultures from a limited number of recording sites (60 microelectrodes), we took advantage of a computational model consisting of a neuronal network made up of 1024 Izhikevich neurons [3]. Network topologies were designed following the canonical architectures scale-free, random, and small-world [4]. Within this approach, the network is dealt as a graph, where each neuron corresponds to a node, and each synaptically connections to an edge. We simulated the spontaneous activity of such neuronal networks, by sweeping the most common parameters used to characterize these graphs, such as clustering coefficient, connection density, etc. [5]. The main finding which emerges is that although all the network configurations determine a mix of spiking, and bursting activity, the scale-free and partially small-world architectures display a critical behavior, and thus self-organization could be influenced by the network architecture.

Conclusions

We used a combined approach involving models and experiments to get an insight into the interplay of topology and dynamics in cortical networks cultured in vitro. Our results showed that different topologies of connectivity may determine different dynamic states and suggest that a scale-free architecture may account for the variability observed in the experimental data by varying the number of hubs.

References

  1. Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits.

    J Neurosci 2003, 23(35):11167-11177. PubMed Abstract | Publisher Full Text OpenURL

  2. Pasquale V, Massobrio P, Bologna LL, Chiappalone M, Martinoia S: Self-organization and neuronal avalanches in networks of dissociated cortical neurons.

    Neuroscience 2008, 153:1354-1369. PubMed Abstract | Publisher Full Text OpenURL

  3. Izhikevich EM: Simple model of spiking neurons.

    IEEE Trans Neur Net 2003, 6:1569-1572. Publisher Full Text OpenURL

  4. Albert R, Barabasi AL: Statistical mechanics of complex networks.

    Rev Mod Phys 2002, 74(1):47-97. Publisher Full Text OpenURL

  5. Bullmore E, Sporns O: Complex brain networks: graph theoretical analysis of structural and functional systems.

    Nature Reviews 2009, 10:186-198. PubMed Abstract | Publisher Full Text OpenURL