Mean Field Theory for a neural population is the lowest order macroscopic description for a neural network. By defining the firing rate probability density of the network, a dynamical equations can be written to describe the system. In usual case, there are two importent element in the equation : the time scale and the gain function. To understand the microscopic correspondence, I introduce the central idea of the derivation.