Cochlear tuning characteristics arise from temporal prediction of natural sounds

The cochlea decomposes incoming sound waveforms into different frequency components along the length of its basilar membrane. The receptor hair cells at the apical end of this resonant membrane are tuned to the lowest sound frequencies, with the preferred sound frequency of hair cell tuning increasing near-exponentially along the length of the membrane towards its basal end. This frequency composition of the sound is then transmitted to the brain by the auditory nerve fibers that innervate the inner hair cells. Hair cells respond to a sound impulse with a temporally asymmetric envelope and the sharpness of their tuning changes as the frequency to which they are most sensitive varies with their position along the basilar membrane. We ask if there is a normative explanation for why the cochlea decomposes sounds in this manner. Inspired by findings in the retina, we propose that cochlear tuning properties may be optimized for temporal prediction. This principle states that the sensory features represented by neurons are optimized to predict immediate future input from recent past input. We show that an artificial neural network optimized for temporal prediction of the immediate future of raw waveforms of natural sounds from their recent past produces tuning properties that resemble those observed in the auditory nerve. Specifically, the model captures the temporally asymmetric impulse responses, the tonotopic distribution and variation in tuning sharpness along the cochlea, and the frequency glide polarity of the impulse responses. These characteristics are not captured by a similar model optimized for compression of the sound waveform, rather than prediction. Given its success in accounting for the tuning properties at various processing levels in the auditory and visual systems, this finding for the cochlea provides further evidence that temporal prediction may be a general principle of sensory processing.