Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloAnalog memristive synapse in spiking networks implementing unsupervised learning
Anno di pubblicazione2016
Autore/iCovi E.; Brivio S.; Serb A.; Prodromakis T.; Fanciulli M.; Spiga S.
Affiliazioni autoriLaboratorio MDM, Istituto per la Microelettronica e i Microsistemi - Consiglio Nazionale delle Ricerche (CNR), Agrate Brianza, Italy; Nano Group, Department of Electronics and Computer Science, University of Southampton, UK; Dipartimento di Scienza Dei Materiali, Università di Milano Bicocca, Milano, MI, Italy
Autori CNR e affiliazioni
  • inglese
AbstractEmerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.
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Altro abstract-
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Pagine da482-1
Pagine a482-13
Pagine totali-
RivistaFrontiers in neuroscience (Online)
Attiva dal 2007
Editore: Frontiers Research Foundation, - Lausanne
Paese di pubblicazione: Svizzera
Lingua: inglese
ISSN: 1662-453X
Titolo chiave: Frontiers in neuroscience (Online)
Titolo proprio: Frontiers in neuroscience (Online)
Titolo abbreviato: Front. neurosci. (Online)
Numero volume della rivista10
Fascicolo della rivista482
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84997235913)
  • ISI Web of Science (WOS) (Codice:000386091900002)
  • PubMed (Codice:27826226)
Parole chiaveMemristive devices, Unsupervised learning, resistive switching, artificial synapse, synaptic plasticity, Neuromorphic computing, spiking neural network, hafnium oxide, spike-time dependent plasticity
Link (URL, URI)
Titolo parallelo-
LicenzaCC BY 4.0
Scadenza embargo-
Data di accettazione07/10/2016
Note/Altre informazioniFile con informazioni supplementari: 2016.00482
Strutture CNR
  • IMM — Istituto per la microelettronica e microsistemi
Moduli/Attività/Sottoprogetti CNR
  • DFM.AD001.075.001 : Real neurons-nanoelectronics Architecture with Memristive Plasticity
Progetti Europei
Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning
Descrizione: Versione pubblicata open access
Tipo documento: application/pdf