The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002) demonstrated that many of the filtering properties of the cochlea could be explained in terms of efficient coding of natural sounds. This model, however, did not account for properties such as phase-locking or how sound could be encoded in terms of action potentials. Here, we extend this theoretical approach with algorithm for learning efficient auditory codes using a spiking population code. Here, we propose an algorithm for learning efficient auditory codes using a theoretical model for coding sound in terms of spikes. In this model, each spike encodes the precise time position and magnitude of a localized, time varying kernel function. By adapting the kernel functions to the statistics natural sounds, we show that, compared to conventional signal representations, the spike code achieves far greater coding efficiency. Furthermore, the inferred kernels show both striking similarities to measured cochlear filters and a similar bandwidth versus frequency dependence.
['Evan C. Smith', 'Michael S. Lewicki']
Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters