A novel methodology to describe neuronal networks activity reveals spatiotemporal recruitment dynamics of synchronous bursting states.

We propose a novel phase based analysis with the purpose of quantifying the periodic bursts of activity observed in various neuronal systems. The way bursts are intiated and propagate in a spatial network is still insufficiently characterized. In particular, we investigate here how these spatiotemporal dynamics depend on the mean connection length.

Understanding the Generation of Network Bursts by Adaptive Oscillatory Neurons.

Experimental and numerical studies have revealed that isolated populations of oscillatory neurons can spontaneously synchronize and generate periodic bursts involving the whole network. Such a behavior has notably been observed for cultured neurons in rodent's cortex or hippocampus. We show here that a sufficient condition for this network bursting is the presence of an excitatory population of oscillatory neurons which displays spike-driven adaptation.

Effect of threshold disorder on the quorum percolation model.

We study the modifications induced in the behavior of the quorum percolation model on neural networks with Gaussian in-degree by taking into account an uncorrelated Gaussian thresholds variability. We derive a mean-field approach and show its relevance by carrying out explicit Monte Carlo simulations. It turns out that such a disorder shifts the position of the percolation transition, impacts the size of the giant cluster, and can even destroy the transition.

Spatio-temporal behaviors of a clock reaction in an open gel reactor.

The concentration profiles along the feeding direction of a one side fed gel reactor are analyzed for the iodate-arsenous acid reaction. Multiplicity of inhomogeneous stationary solutions is derived. It is also shown that such profiles may undergo oscillatory bifurcations under long range activation conditions.