  We have analyzed the synchronization of a small-world network of chaotic
Rulkov neurons with an electrical coupling that contains a delay. We have
developed an algorithm to compute a certain delay whose result is to improve
the synchronization of the network when it was slightly synchronized, or to get
synchronized when it was desynchronized. Our general approach has been to use
tools from signal analysis, such as Fourier and wavelet transforms. With these
tools, we have characterized the behavior of the neurons for different
parameters in frequency and time-frequency domains. Finally, the robustness of
the algorithm has been tested by using non-homogeneous neurons affected with a
parametric noise.
