Evan Smith1 & Michael S. Lewicki2
evan+@cnbc.cmu.edu lewicki@cnbc.cmu.edu
Departments of Psychology1 & Computer Science2 Center for the Neural Basis of Cognition Carnegie Mellon University
To whom correspondence should be addressed.
Abstract
The auditory neural code must serve a wide range of auditory tasks that require exquisite sensitivity in time and frequency and be effective over the diverse array of sounds present in natural acoustic environments. It has been suggested1–5 that sensory systems may have evolved highly efficient coding strategies in order to maximize the information conveyed to the brain while minimizing the required energy and neural resources. Here we show that, for natural sounds, the complete acoustic waveform can be represented efficiently using a non-linear model based on a population spike code. In this model, idealized spikes encode the precise temporal positions and magnitudes of underlying acoustic features. We find when the features are optimized for coding either natural sounds or speech, they show striking similarities to time-domain cochlear filter estimates, exhibit a frequency-bandwidth dependence similar to that of auditory nerve fibers, and yield significantly greater coding efficiency compared to conventional signal representations. These results suggest that the auditory code approaches an information theoretic optimum and that the acoustic structure of speech is adapted to the coding capacity of the mammalian auditory system.