This paper explores the use of volatile resistive switching memory (RRAM) devices to implement neuromorphic circuits for memristive tonotopic mapping, inspired by the human auditory system. The authors demonstrate the implementation of key neuromorphic primitives, such as logarithmic integration and tonotopic mapping, using RRAM-based circuits. They show that these circuits can effectively process temporal signals, particularly in the context of speech recognition. The study highlights the potential of memristive devices for energy-efficient, high-density neuromorphic systems, leveraging their intrinsic dynamics and volatility to achieve spatiotemporal signal processing. The results support the use of memristive devices for physical processing of temporal signals, paving the way for more efficient and scalable neuromorphic computing systems.This paper explores the use of volatile resistive switching memory (RRAM) devices to implement neuromorphic circuits for memristive tonotopic mapping, inspired by the human auditory system. The authors demonstrate the implementation of key neuromorphic primitives, such as logarithmic integration and tonotopic mapping, using RRAM-based circuits. They show that these circuits can effectively process temporal signals, particularly in the context of speech recognition. The study highlights the potential of memristive devices for energy-efficient, high-density neuromorphic systems, leveraging their intrinsic dynamics and volatility to achieve spatiotemporal signal processing. The results support the use of memristive devices for physical processing of temporal signals, paving the way for more efficient and scalable neuromorphic computing systems.