Bistable firing pattern in a neural network model
P. R. Protachevicz, F. S. Borges, E. L. Lameu, P. Ji, K. C. Iarosz, A., H. Kihara, I. L. Caldas, J. D. Szezech Jr., M. S. Baptista, E. E. N. Macau,, C. G. Antonopoulos, A. M. Batista, J. Kurths

TL;DR
This paper investigates how neural networks with excitatory and inhibitory synapses exhibit bistability and hysteresis in synchronization, revealing mechanisms that can trigger or suppress epileptic seizures through external stimulation.
Contribution
It demonstrates the presence of bistability and hysteresis in neural synchronization, and shows how external stimulation can control abnormal synchrony related to epilepsy.
Findings
Bistability leads to hysteresis in neural synchronization.
External stimulation can trigger or suppress epileptic-like seizures.
Reduced inhibitory influence increases synchronization.
Abstract
Excessively high, neural synchronisation has been associated with epileptic seizures, one of the most common brain diseases worldwide. A better understanding of neural synchronisation mechanisms can thus help control or even treat epilepsy. In this paper, we study neural synchronisation in a random network where nodes are neurons with excitatory and inhibitory synapses, and neural activity for each node is provided by the adaptive exponential integrate-and-fire model. In this framework, we verify that the decrease in the influence of inhibition can generate synchronisation originating from a pattern of desynchronised spikes. The transition from desynchronous spikes to synchronous bursts of activity, induced by varying the synaptic coupling, emerges in a hysteresis loop due to bistability where abnormal (excessively high synchronous) regimes exist. We verify that, for parameters in the…
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Taxonomy
TopicsNeural dynamics and brain function · stochastic dynamics and bifurcation · Nonlinear Dynamics and Pattern Formation
