1 - Simulation of Large Networks: Technique and Progress

Abstract

Computer models of epilepsy and seizures require simulation of large networks in order to produce emergent phenomenology, but also require inclusion of details of cellular and synaptic physiology to retain connection with pharmacological effectors for therapeutic intervention. The former constraint suggests the use of simplifying approaches: integrate-and fire models or meanfield approximations. The latter constraint pushes one towards multi-compartment models. Compromise is required. Highly-simplified event-driven complex artificial-cell models that utilize a series of rules to emulate critical underlying phenomena likely important for seizure generation and patterning, have been developed. These include depolarization blockade, voltage-dependent NMDA activation, and after hyperpolarization. Using these units, one can readily run simulations of 1–2 million units on a single processor. This chapter presents results of data-mining 138,240 simulations of a 3,500-unit network. Parameter explorations can then relate single-unit dynamical “structure” to network function. It is found that the networks are prone to latch-up, reminiscent of a seizure tonic phase. This can alternate with a slow repetitive clonic-like activity. Parameter dependence of epileptiform features was typically complex. This consequence of network complexity fits with the multi-site activity of endogenous neuroeffectors and may help explain why “dirty” drugs, those acting at multiple sites, might be particularly effective for seizure control.