In silico framework for benchmarking optogenetic hearing restoration

OBJECTIVE: Cochlear implants (CI) partially restore hearing in profoundly hearing-impaired or deaf individuals by electrically stimulating the auditory nerve. A primary bottleneck in electrical 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. Here we established an in silico framework, FraSCO, to model sound encoding in the human cochlea by an optical CI (oCI) for testing the potential of optogenetic hearing restoration. APPROACH: The biophysical modelling 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 optogenetic and electrical stimulation to the spectrogram of the sound applied. The model incorporated technologically feasible properties of the oCIs with 64 stimulation channels. MAIN RESULTS: The biophysical modelling 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 axis to represent sound encoding, we found that improved spectral selectivity achieves comparable information encoding to electrical implants despite lower stimulation rates in current implementations of optogenetic stimulation. We also found that potential oCI users are predicted to be less impacted by noisy backgrounds. SIGNIFICANCE: The established computational framework enables an in silico investigation and benchmarking of sound encoding in the cochlea by future oCI and state-of-the-art eCI. The results of the exemplary application indicate that optogenetic sound encoding has potential to improve speech understanding in noisy environments for CI users.