  Epilepsy is a dynamic and complex neurological disease affecting about 1% of
the worldwide population, among which 30% of the patients are drug-resistant.
Epilepsy is characterized by recurrent episodes of paroxysmal neural discharges
(the so-called seizures), which manifest themselves through a large-amplitude
rhythmic activity observed in depth-EEG recordings, in particular in local
field potentials (LFPs). The signature characterizing the transition to
seizures involves complex oscillatory patterns, which could serve as a marker
to prevent seizure initiation by triggering appropriate therapeutic
neurostimulation methods. To investigate such protocols, neurophysiological
lumped-parameter models at the mesoscopic scale, namely neural mass models, are
powerful tools that not only mimic the LFP signals but also give insights on
the neural mechanisms related to different stages of seizures. Here, we analyze
the multiple time-scale dynamics of a neural mass model and explain the
underlying structure of the complex oscillations observed before seizure
initiation. We investigate population-specific effects of the stimulation and
the dependence of stimulation parameters on synaptic timescales. In particular,
we show that intermediate stimulation frequencies (>20 Hz) can abort seizures
if the timescale difference is pronounced. Those results have the potential in
the design of therapeutic brain stimulation protocols based on the
neurophysiological properties of tissue.
