An integrate and fire neural network to simulate epileptic patterns in intracortical EEG

Epilepsy is characterized by paradoxical patterns of neural activity, either localized within single regions of the cortex or spreading to large areas. These patterns may cause different types of EEG, which may dynamically change during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, in cases when excitation dominates over inhibition. The aim of this work is to use a neural network of integrate and fire neurons to analyze which parameter alterations (either at the neural level or at the level of synapses) may induce network instability and epileptic-like discharges. A signal representative of EEG is simulated by summing the membrane potential changes of all neurons. Model simulations show that an increase in the strength and in spatial extension of excitatory synapses vs. inhibitory synapses and/or an increase in the relative refractory period, may determine sustained patterns of uncontrolled activity, which propagate along the network. These propagating waves may have a different shape and frequency, depending on the particular parameter set used during the simulations and on initial random conditions. The resulting model EEG signals include isolated or repeated bursts, high-frequency low-amplitude waves or larger oscillations with lower frequency. A derangement in a few parameters of the model causes the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during intracortical EEG measurements in epilepsy. The obtained results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns, often observed during seizure, with the underlying alterations at the neural and network levels