Recurrent cortical networks with realistic horizontal connectivities show complex dynamics
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KeywordsFiring Rate Input Rate High Firing Rate Poissonian Input Regular Firing
Network dynamics are simulated using NEST/PyNN . They are based on 50.000 neurons, randomly distributed in 5 squared millimeters with a global connectivity c ~0.0125. We consider conductance-based integrate-and-fire neurons with regular spiking excitatory and fast spiking inhibitory cells. Varying the poissonian input rates and the numeric relation between excitatory and inhibitory synaptic strength, we explore the phase spaces of different networks. In addition, we apply spatially restricted activity injections, i.e., a group of neighboring neurons receives additional input.
Results and discussion
Similar to previous studies we observe synchronous regular firing for high input rates combined with low inhibition, while small rates and high inhibition results in asynchronous irregular firing. The amount of local connections influences the boundaries at which the network switches between states, and the interesting input parameter range changes accordingly. Non-random networks provide significantly higher firing rates, as well as sharper transitions. These networks exhibit "new" activity patterns that indicate the spatio-temporal spread of activity that random networks cannot account for.
This work is based on the PhD project  and supported by EU Grant 15879 (FACETS).
This article is published under license to BioMed Central Ltd.