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 International Conference on Complex Systems (ICCS2007)

The development of synchronized bursts and distinctive topological patterns during neural network organization.

Einat Fuchs
Dept. of Zoology, Tel Aviv University

Amir Ayali
Dept. of Zoology, Tel Aviv University

Eyal Hulata
School of Physics and Astronomy, Tel Aviv Univesity

Stefano Boccaletti
Institute for Complex Systems, Florence Italy

Eshel Ben-Jacob
School of Physics and Astronomy, Tel Aviv Univesity

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     Last modified: May 31, 2007

Synchronized oscillatory and rhythmic patterns are widespread in the brain, playing important roles in all aspects of the function of our nervous system. The aim of the current study is to understand the development of synchronized bursts as information-bearing neuronal activity patterns. We monitored the morphological organization and spontaneous activity of neuronal networks cultured on multi-electrode-arrays (MEA) during their self-executed evolvement from a mixture of dissociated cells into an active network. Complex collective network electrical activity evolved from sporadic firing patterns of the single neurons. Network activity was marked by bursting events with inter-neuronal synchronization and non-arbitrary temporal ordering. We quantified these individual-to-collective activity transitions using newly-developed system level measures of time series regularity and complexity. We found that individual neuronal activity before synchronization was characterized by high regularity and low complexity. During neuronal wiring there was a transient period of re-organization marked by low regularity, which then leads to co-emergence of elevated regularity and functional (non-stochastic) complexity. We further investigated the morphology-activity interplay by modeling artificial neuronal networks with different topological organizations and connectivity schemes. The simulations support our experimental results by showing increased levels of complexity of neuronal activity patterns when neurons are wired up and organized in clusters (similar to mature real networks), as well as network-level activity regulation once collective activity forms.

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