Abstract
In this short talk we'll describe a class of markovian approximations that reduce the complexity of spiking-neural-networks, allowing for efficient estimation of certain dynamical parameters, such as firing-rates and steady-state membrane potential distribution. These markovian approximations can be used in several practical applications, mapping biological parameters to estimates of firing rate.