  Neurostimulation is a promising therapy for abating epileptic seizures.
However, it is extremely difficult to identify optimal stimulation patterns
experimentally. In this study human recordings are used to develop a functional
24 neuron network statistical model of hippocampal connectivity and dynamics.
Spontaneous seizure-like activity is induced in-silico in this reconstructed
neuronal network. The network is then used as a testbed to design and validate
a wide range of neurostimulation patterns. Commonly used periodic trains were
not able to permanently abate seizures at any frequency. A simulated annealing
global optimization algorithm was then used to identify an optimal stimulation
pattern which successfully abated 92% of seizures. Finally, in a fully
responsive, or "closed-loop" neurostimulation paradigm, the optimal stimulation
successfully prevented the network from entering the seizure state. We propose
that the framework presented here for algorithmically identifying
patient-specific neurostimulation patterns can greatly increase the efficacy of
neurostimulation devices for seizures.
