Optimal hearing aid design through restoration of the neural code
Journal:
bioRxiv
Published Date:
Feb 4, 2026
Abstract
Hearing loss introduces complex distortions in the neural coding of sound that current hearing aids fail to address. Here, we leverage tools for neural control via deep learning to identify novel sound processing strategies to correct these distortions. We use large-scale intracranial recordings from the gerbil inferior colliculus to train deep neural network models of neural coding (ICNets) to serve as in silico surrogates for brains with normal and impaired hearing. We then use the ICNets to train another network (AidNet) to act as an optimal hearing aid, providing the individualized sound processing required to elicit normal neural activity in impaired brains. We find that AidNet outperforms state-of-the-art hearing aid processing by a wide margin in correcting both distortions in neural coding and deficits in simulated phoneme recognition. This successful demonstration of neural restoration via closed-loop optimization opens up new possibilities for improving not only hearing aids, but also other sensory devices and neurotechnologies.
