Rule-based firing for network simulations

We have developed a rule-based firing model that reproduces some of the complexity of real neurons with little computational overhead and isolation of postsynaptic state variables that are likely to be critical for network dynamics. The basic rule remains the same as that of the integrate-and-fire model: fire when the state variable exceeds a fixed threshold. Additional rules were added to provide adaptation, bursting, depolarization blockade, Mg-sensitive NMDA conductance, anode-break depolarization, and others. The implementation is event driven, providing additional speed-up by avoiding numerical integration.