Two stages of bandwidth scaling drives efficient neural coding of natural sounds

摘要

Theories of efficient coding propose that the auditory system is optimized for the statistical structure of natural sounds, yet the transformations underlying optimal acoustic representations are not well understood. Using a database of natural sounds including human speech and a physiologically-inspired auditory model, we explore the consequences of peripheral (cochlear) and mid-level (auditory midbrain) filter tuning transformations on the representation of natural sound spectra and modulation statistics. Whereas Fourier-based sound decompositions have constant time-frequency resolution at all frequencies, cochlear and auditory midbrain filters bandwidths increase proportional to the filter center frequency. This form of bandwidth scaling produces a systematic decrease in spectral resolution and increase in temporal resolution with increasing frequency. Here we demonstrate that cochlear bandwidth scaling produces a frequency-dependent gain that counteracts the tendency of natural sound power to decrease with frequency, resulting in a whitened output representation. Similarly, bandwidth scaling in mid-level auditory filters further enhances the representation of natural sounds by producing a whitened modulation power spectrum (MPS) with higher modulation entropy than both the cochlear outputs and the conventional Fourier MPS. These findings suggest that the tuning characteristics of the peripheral and mid-level auditory system together produce a whitened output representation in three dimensions (frequency, temporal and spectral modulation) that reduces redundancies and allows for a more efficient use of neural resources. This hierarchical multi-stage tuning strategy is thus likely optimized to extract available information and may underlies perceptual sensitivity to natural sounds. Author summaryTheory suggests that the auditory system evolved to optimally encode salient structure in natural sounds-maximizing perceptual capabilities while minimizing metabolic demands. Here, using a multi-stage model of the auditory system and a collection of environmental sounds, including vocalizations such as speech, we demonstrate how auditory responses may be optimized for equalizing the power distribution of natural sounds at two levels. This processing strategy may improve the allocation of resources throughout the auditory pathway, while ensuring that a broad range of auditory features can be detected and perceived. Such a multi-stage strategy for processing natural sounds likely contributes to human perceptual capabilities and adopting such a code could enhance the performance of auditory prosthetics and machine systems for sound recognition.