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Introduction. Computational models of optic nerve stimulation can allow to estimate the neural response of optic nerve fibers electrical stimulation and thus can be exploited to tune stimulation parameters to obtain a specific target perception. In principle, such tuning should be performed in an automatic way, so that the chosen stimulation parameters minimize some given cost function related to the quality of resulting the visual perception, but the use of such automatic methods is still under study and no satisfactory solution is available yet. In the absence of automatic methods, stimulation parameters are customarily set via manual tuning, which can be extremely time-consuming if performed in a non-principled way. Methods. We build biophysically-accurate hybrid models of monopolar and bipolar electrical stimulation of optic nerve fibers to study how the fibers firing rates depend upon the stimulation parameters. Results. In the case of monopolar sinusoidal stimulation of optic nerve fibers, we show that the amplitude of stimulation controls the size of the recruited cluster of fibers, and that the frequency controls their firing rate, independently. Instead, for bipolar stimulation, we show that when cross-talk is non negligible it is very difficult to obtain rules of thumb linking the firing rate of target fibers to stimulation parameters. Conclusion. We show that, if the stimulation amplitude is kept such that neighboring stimulating sites do not produce cross-talk, it is possible to reconstruct visual scenes “pixel-by-pixel” without needing any optimization process. If on the contrary current steering is required and cross-talk is non negligible, then it is very difficult to obtain rules of thumb and the development and use of automatic optimization techniques should be preferable. |
