Encoding of Information Into Neural Spike Trains in an Auditory Nerve Fiber Model With Electric Stimuli in the Presence of a Pseudospontaneous Activity

This paper presents an information-theoretic analysis of neural spike trains in an auditory nerve fiber (ANF) model stimulated extracellularly with Gaussian or sinusoidal waveforms in the presence of a pseudospontaneous activity of spike firings. In the computer simulation, stimulus current waveforms were applied 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, a post-stimulus time histogram (PSTH) was generated, and raster plots were depicted for 30 stimulus presentations, in order to investigate the temporal precision and reliability of the spike firing times. Also, inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and the information rate of the spike trains. It was shown in the case of Gaussian electric stimuli that the temporal precision of spike firing times and the reliability of spike firings were found to increase as the standard deviation (SD) of the Gaussian electric stimuli increased. It was also shown in the case of sinusoidal electric stimuli where there was a specific amplitude of sinusoidal waveforms, the information rate being maximized. It was implied that setting the parameters of electric stimuli to the specific values which maximize the information rate might contribute to efficiently encoding information into the spike trains in the presence of a pseudospontaneous activity of spike firings