  We present a modified library-based method for simulating the Hodgkin-Huxley
(HH) neuronal networks. By pre-computing a high resolution data library during
the interval of an action potential (spike), we can avoid evolving the HH
equations during the spike and can use a large time step to raise efficiency.
The library method can stably achieve at most 10 times of speedup compared with
the regular Runge-Kutta method while capturing most statistical properties of
HH neurons like the distribution of spikes which data is widely used in the
statistical analysis like transfer entropy and Granger causality. The idea of
library method can be easily and successfully applied to other HH-type models
like the most prominent \textquotedblleft regular spiking\textquotedblright ,
\textquotedblleft fast spiking\textquotedblright , \textquotedblleft
intrinsically bursting\textquotedblright{} and \textquotedblleft low-threshold
spike\textquotedblright{} types of HH models.
