In silico framework for benchmarking optogenetic hearing restoration

Authors

Khurana, L., Nejedly, P., Jagger, D., Moser, T., Jablonski, L.

Abstract

Background. Cochlear implants (CI) partially restore hearing in profoundly hearing im-paired or deaf people by electrically stimulating the auditory nerve. A bottleneck of electri-cal CIs is the broad spread of electrical current from each electrode that limits the transfer of spectral information, which might be overcome by future spatially confined optogenetic stimulation. Objective. Here we established an in silico framework, FraSCO, to model sound encoding in the human cochlea by an optogenetic CI (oCI) for testing the potential of optogenetic hearing restoration. Methods. The biophysical modeling framework combined an optical ray tracing model implementing a human cochlea implanted with a waveguide-based oCI with a single compartment model of optogenetically modified spiral ganglion neurons (SGNs). The input was an optogenetic sound coding strategy and the quality of the neural representation was evaluated based on comparison of neurograms evoked by opto-genetic and electrical stimulation to the spectrogram of the sound applied. The model aimed for technologically feasible properties of the oCIs with 64 stimulation channels. Re-sults. The biophysical modeling framework successfully captured essential physiological features of optogenetic SGN stimulation with a minimal set of ion channel types expressed in the SGN soma. Working with a sample of 1000 SGNs distributed along the tonotopic ax-is to represent sound encoding, we found that improved spectral selectivity more than com-pensates for lower temporal fidelity of current implementations of optogenetic stimulation. Significance. The established computational framework enables in silico investigation and benchmarking of sound encoding in the cochlea by future oCI and state-of-the-art eCI. The results indicate that optogenetic sound encoding has potential to improve speech under-standing in noisy environments for CI users.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 21 Jul 2026.

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