Bistable firing pattern in a neural network model
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 bistability regime, a square current pulse can trigger excessively high (abnormal) synchronisation, a process that can reproduce features of epileptic seizures. Then, we show that it is possible to suppress such abnormal synchronisation by applying a small-amplitude external current on less than 10% of the neurons in the network. Our results demonstrate that external electrical stimulation not only can trigger synchronous behaviour, but more importantly, it can be used as a means to reduce abnormal synchronisation and thus, control or treat effectively epileptic seizures.
Keywords
Cite
@article{arxiv.1810.10142,
title = {Bistable firing pattern in a neural network model},
author = {P. R. Protachevicz and F. S. Borges and E. L. Lameu and P. Ji and K. C. Iarosz and A. H. Kihara and I. L. Caldas and J. D. Szezech and M. S. Baptista and E. E. N. Macau and C. G. Antonopoulos and A. M. Batista and J. Kurths},
journal= {arXiv preprint arXiv:1810.10142},
year = {2019}
}