A fast and flexible approximation of power-law adaptation for auditory computational models

Abstract

1.AbstractPower-law adaptation is a form of neural adaptation that has been shown to provide a better description of auditory-nerve adaptation dynamics as compared to simpler exponential-adaptation processes. However, the computational costs associated with power-law adaptation are high and, problematically, grow superlinearly with the number of samples in the simulation. This cost limits the applicability of power-law adaptation in simulations of responses to relatively long stimuli, such as speech, or in simulations for which high sampling rates are needed. Here, we present a simple approximation to power-law adaptation based on a parallel set of exponential-adaptation processes with different time constants, demonstrate that the approximation improves on an existing approximation provided in the literature, and provide updates to a popular phenomenological model of the auditory periphery that implements the new approximation.