A Machine Learning-based Neural Implant Front End for Inducing Naturalistic Firing.

Despite being able to restore speech perception with 99% success rate, cochlear implants cannot successfully restore pitch perception or music appreciation. Studies suggest that if auditory neurons were activated with fine timing closer to that of natural responses pitch would be restored. Predicting the timing of cochlear responses requires detailed biophysical models of sound transmission, inner hair cell responses, and outer hair cell responses. Performing these calculations is computationally costly for real time cochlear implant stimulation. Instead, implants typically modulate pulse amplitude of fixed pulse rate stimulation with the band-limited envelopes of incoming sound. This method is known to produce unrealistic responses, even to simple step inputs. Here we investigate using a machine learning algorithm to optimize the prediction of the desired firing patterns of the auditory afferents in response to sinusoidal and step modulation of pure tones. We conclude that a trained network that consists of 25 GRU nodes can reproduce fine timing with 4.4 percent error on a test set of sines and steps. This trained network can also transfer learn and capture features of natural sounds that are not captured by standard CI algorithms. Additionally, for 0.5 second test inputs, the ML algorithm completed the sound to spike rate conversion in 300x less time than the phenomenological model. This calculation occurs at a real-time compatible rate of 1 ms for 1 second of spike timing prediction on an i9 microprocessor. This suggests that this is a feasible approach to pursue for real-time CI implementation.