Scientific Reports · 2019 · 18 citations · 75 references
Life SciencesNeurobiologyNeuroscienceComputational NeuroscienceNeurophysiologySystems NeuroscienceBrain ConnectivityCortical NetworksNetwork DynamicsNeural Circuit DynamicsNeural NetworksNeuronal DynamicsBrain RegionsDynamicsNeuronal Network DynamicsNeuronal SynchronizationPopulation SynchronizationSynchronizationSynchronization DynamicsSynchronization In Neural Networks
In recent years, diverse studies have reported that different brain regions, which are internally densely connected, are also highly connected to each other. This configuration seems to play a key role in integrating and interchanging information between brain areas. Also, changes in the rich-club connectivity and the shift from inhibitory to excitatory behavior of hub neurons have been associated with several diseases. However, there is not a clear understanding about the role of the proportion of inhibitory/excitatory hub neurons, the dynamic consequences of rich-club disconnection, and hub inhibitory/excitatory shifts. Here, we study the synchronization and temporal correlations in the neural Izhikevich model, which comprises excitatory and inhibitory neurons located in a scale-free hierarchical network with rich-club connectivity. We evaluated the temporal autocorrelations and global synchronization dynamics displayed by the system in terms of rich-club connectivity and hub inhibitory/excitatory population. We evaluated the synchrony between pairs of sets of neurons by means of the global lability synchronization, based on the rate of change in the total number of synchronized signals. The results show that for a wide range of excitatory/inhibitory hub ratios the network displays 1/f dynamics with critical synchronization that is concordant with numerous health brain registers, while a network configuration with a vast majority of excitatory hubs mostly exhibits short-term autocorrelations with numerous large avalanches. Furthermore, rich-club connectivity promotes the increase of the global lability of synchrony and the temporal persistence of the system.
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Simple model of spiking neurons
Eugene M. Izhikevich · IEEE Transactions on Neural Networks · 2003 · 4.8K citations
Synchronization in complex networks
Àlex Arenas, Albert Dı́az-Guilera, Jürgen Kurths et al. · Physics Reports · 2008 · 3.7K citations · Full text
The Kuramoto model: A simple paradigm for synchronization phenomena
Juan A. Acebrón, L. L. Bonilla, C. J. Pérez Vicente et al. · Reviews of Modern Physics · 2005 · 3.5K citations · Full text
Natural Sciences, Biology, Physics +16