We describe a computational model of @entity302 mimicking the activity exhibited by an animal model of @entity302 in vitro. The computational model permits generation of synthetic data to assist in the evaluation of new algorithms for @entity302 treatment via adaptive neurostimulation. The model implements both single-compartment pyramidal neurons and fast-spiking interneurons, arranged in a one-dimensional network using both excitatory and inhibitory synapses. The model tracks changes in extracellular ion concentrations, which determine the reversal potentials of membrane currents. Changes in simulated ion concentration provide positive feedback which drives the system towards the epileptiform state. One mechanism of positive feedback explored by this model is the conversion of pyramidal cells from regular spiking to intrinsic bursting as extracellular @entity972 concentration increases. One of the main contributions of this work is the development of a slow @entity308 mechanism that enforces @entity410 termination. The network spontaneously leaves the @entity410 -like state as the slow @entity308 variable decreases. This is one of the first detailed computational models of @entity302 , which exhibits realistic transitions between inter- @entity410 and @entity410 states, and back, with state durations similar to the in vitro model. We validate the computational model by comparing its state durations to those of the biological model. We also show that electrical stimulation of the computational model achieves @entity410 suppression comparable to that observed in the in vitro model.

A bistable computational model of recurring XXXX as observed in rodent slice preparations.

@entity410 :: ('MESH:D012640', 'Disease') :: ['seizure'] @entity308 :: ('MESH:D003866', 'Disease') :: ['depression'] @entity302 :: ('MESH:D004827', 'Disease') :: ['epilepsy', 'epileptiform activity'] @entity972 :: ('MESH:D011188', 'Chemical') :: ['potassium']
@entity302 :: (MESH:D004827,Disease) :: ['epileptiform activity']
