This paper explores the use of volatile resistive random access memory (RRAM) devices for implementing memristive tonotopic mapping, inspired by the human auditory system. The researchers demonstrate logarithmic integration and tonotopic mapping of signals using a generalized stochastic device-level approach with volatile RRAM devices. The tonotopic classification is shown to be suitable for speech recognition. This work suggests that memristive devices are promising for energy-efficient, high-density neuromorphic systems capable of processing temporal signals.
