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Memristive tonotopic mapping with volatile resistive switching memory devices

Author

Listed:
  • Alessandro Milozzi

    (Politecnico di Milano and IU.NET)

  • Saverio Ricci

    (Politecnico di Milano and IU.NET)

  • Daniele Ielmini

    (Politecnico di Milano and IU.NET)

Abstract

To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems.

Suggested Citation

  • Alessandro Milozzi & Saverio Ricci & Daniele Ielmini, 2024. "Memristive tonotopic mapping with volatile resistive switching memory devices," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47228-1
    DOI: 10.1038/s41467-024-47228-1
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    References listed on IDEAS

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    1. Jaehyun Kang & Taeyoon Kim & Suman Hu & Jaewook Kim & Joon Young Kwak & Jongkil Park & Jong Keuk Park & Inho Kim & Suyoun Lee & Sangbum Kim & YeonJoo Jeong, 2022. "Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
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