Optimal hearing aid design through restoration of the neural code

Hearing loss introduces complex distortions in the neural coding of sound that current hearing aids fail to address. Here, we combine electrophysiology and 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 stat