Memristive tonotopic mapping with volatile resistive switching memory devices.

Journal

Nature communications

ISSN: 2041-1723

Titre abrégé: Nat Commun

Pays: England

ID NLM: 101528555

Informations de publication

Date de publication:
01 Apr 2024

Historique:

received: 27 07 2023

accepted: 25 03 2024

medline: 2 4 2024

pubmed: 2 4 2024

entrez: 1 4 2024

Statut: epublish

Résumé

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.

Identifiants

DOI: 10.1038/s41467-024-47228-1 PMID: 38561389

pubmed: 38561389

doi: 10.1038/s41467-024-47228-1

pii: 10.1038/s41467-024-47228-1

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

2812

Subventions

Organisme : EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)

ID : 899559

Informations de copyright

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