On modeling of a recurrent neural network from neural spiking data.

We present a theoretical and computational work, aiming at the estimation of firing rate based excitatory inhibitory neural network from realistic stimulus-response data. The stimulus response recordings are taken previous study which performs measurement on H1 neurons order Diptera flies. parameter is performed by maximum likelihood method. As data single recording 20 minutes, it segmented individual segments superimposed each other to increase statistical content information. true values model parameters unknown as we not using synthetic Because this fact, two sample Kolmogorov-Smirnov test applied compare interspiking intervals recorded responses. Estimation analysis results presented in tabular graphical forms. In addition, comparison with research employing modified Fitzhugh-Nagumo made.