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This article presents an analysis of the information rate of neural spike trains in an auditory nerve fiber (ANF) model stimulated extracellularly by colored Gaussian electric stimuli. In the analysis, stimulus current waveforms were generated by convolving α's functions with some α's parameters (the inverse of time constants) to white Gaussian processes, and were presented repeatedly to a stimulating electrode located 1 mm above the 26th node of Ranvier, in an ANF axon model having 50 nodes of Ranvier, each consisting of stochastic sodium and potassium channels. From spike firing times recorded at the 36th node of Ranvier, the inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and information rate of the spike trains. In the present article, it is shown that at a specific α parameter, the information rate was found to be maximized. It was implied that setting stimulus parameters to the specific values which maximize the information rate might contribute to efficiently encoding information in neural prostheses.