A population density method for large-scale modeling of neuronal networks with realistic synaptic kinetics

Population density function (PDF) methods have been used as both a time-sav ing alternative to direct Monte-Carlo simulation of neuronal network activi ty and as a tool for the analytic study of neuronal networks. Computational efficiency of the PDF method is dependent on a low-dimensional state space for the underlying individual neuron. Many previous implementations have a ssumed that the time scale of the synaptic kinetics is very fast on the sca le of the membrane time constant in order to obtain a one-dimensional state space. Here, we extend our previous PDF methods for synapses with realisti c kinetics; synaptic current injection for inhibition is replaced with more realistic conductance modulation. (C) 2001 Published by Elsevier Science B .V.