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

Power-law adaptation is a form of neural adaptation that has been recently implemented in a popular model of the mammalian auditory nerve to explain responses to modulated sound and adaptation over long time scales. However, the high computational cost of power-law adaptation, especially for longer simulations, means it must be approximated to be practically usable. Here, a straightforward scheme to approximate power-law adaptation is presented, demonstrating that the approximation improves on an existing approximation provided in the literature. Code that implements the new approximation is provided.