Phase synchronization phenomena of neuronal networks are one of many features depicted by real networks that can be studied using computational models. Here, we proceed with numerical simulations of a globally connected network composed of non-identical (distinct) Izhikevich neuron model to study clustered phase synchronization. We investigate the case in which, once coupled, there exist two main neuron clusters in the network: one of them is bi-stable, depicting phase-synchronized or unsynchronized states, depending on the initial conditions; and the second one shows just an unsynchronized state. For the set of initial conditions that lead the first cluster to the synchronized regime, we observe a chimera-like pattern of the network. For small networks, the dynamics can also present intermittent chimera-like scenarios. In this context, the mechanism for intermittent chimera states is based on two features: the coexistence of a synchronized cluster with an unsynchronized one; and the capability of one cluster to display bi-stability depending on the signal trait by which it is forced. We conclude with an understanding of intermittent chimera-like dynamics as the limit case where bi-stability is not maintained, which occurs due to the loss of uniformity in the neuron input synaptic currents.

See here a short video. Poster below…

2 thoughts on “Poster 2022#10 – Gabriel MARGHOTI – Intermittent chimera-like and bi-stable synchronization states in network of distinct Izhikevich neurons [VIDEO]

  1. Thank you Greg, I believe a parameter associated with the recovery rate of an internal resource necessary for the neuron to spike could impact the number of spikes in a burst. The same bifurcation occurs for a variation in parameter ‘I’, which is associated with the input bias a neuron receives.

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