@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00935190v1> a dcat:Dataset ;
    dct:description """
              Research in the field of neuromorphic- and cognitive- computing has generated a lot of interest in recent years. With potential application in fields such as large-scale data driven computing, robotics, intelligent autonomous systems to name a few, bio-inspired computing paradigms are being investigated as the next generation (post-Moore, non-Von Neumann) ultra-low power computing solutions. In this work we discuss the role that different emerging non-volatile resistive memory technologies (RRAM), specifically (i) Phase Change Memory (PCM), (ii) Conductive-Bridge Memory (CBRAM) and Metal-Oxide based Memory (OXRAM) can play in dedicated neuromorphic hardware. We focus on the emulation of synaptic plasticity effects such as long-term potentiation (LTP), long term depression (LTD) and spike-timing dependent plasticity (STDP) with RRAM synapses. We developed novel low-power architectures, programming methodologies, and simplified STDP-like learning rules, optimized specifically for some RRAM technologies. We show the implementation of large-scale energy efficient neuromorphic systems with two different approaches (i) deterministic multi-level synapses and (ii) stochastic-binary synapses. Prototype applications such as complex visual- and auditory- pattern extraction are also shown using feed-forward spiking neural networks (SNN). We also introduce a novel methodology to design low-area efficient stochastic neurons that exploit intrinsic physical effects of CBRAM devices.
            """ ;
    dct:identifier "NNT: 2013GRENT023" ;
    dct:issued "2026-05-07T07:11:37.476997"^^xsd:dateTime ;
    dct:language "fr" ;
    dct:modified "2026-05-07T07:11:37.477002"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Emerging Resistive Memory Technology for Neuromorphic Systems and Applications" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00935190v1/resource/27d77090-2985-472a-886b-4f88827430a8> ;
    dcat:keyword "cognitive-computing",
        "conductive-bridge-memory-cbram",
        "extraction-de-motifs",
        "infoeu-reposemanticsdoctoralthesis",
        "informatique-cognitive-visuelle-et-auditive",
        "memoire-a-changement-de-phase",
        "neuromorphic-computing",
        "phase-change-memory-pcm",
        "reseau-de-neurones-artificiels",
        "resistive-oxide-memor",
        "spiking-neural-networks",
        "spiotherengineering-sciences-physicsother",
        "synapse-de-memoire-resistive",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00935190> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00935190v1/resource/27d77090-2985-472a-886b-4f88827430a8> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-07T07:11:37.487910"^^xsd:dateTime ;
    dct:modified "2026-05-07T07:11:37.457994"^^xsd:dateTime ;
    dct:title "Emerging Resistive Memory Technology for Neuromorphic Systems and Applications" ;
    dcat:accessURL <https://theses.hal.science/tel-00935190> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00935190> a foaf:Document .