OPTIMIZING SYNAPTIC CONDUCTANCE CALCULATION FOR NETWORK SIMULATIONS

High computational requirements in realistic neuronal network simulati ons have led to attempts to realize implementation efficiencies while maintaining as much realism as possible. Since the number of synapses in a network will generally far exceed the number of neurons, simulati on of synaptic activation may be a large proportion of total processin g time. We present a consolidating algorithm based on a recent biophys ically-inspired simplified Markov model of the synapse. Use of a singl e lumped state variable to represent a large number of converging syna ptic inputs results in substantial speed-ups.