Open AccessExtended memory lifetime in spiking neural networks employing memristive synapses with nonlinear conductance dynamics
Author(s) -
Stefano Brivio,
Daniele Conti,
Manu V Nair,
Jacopo Frascaroli,
Erika Covi,
Carlo Ricciardi,
Giacomo Indiveri,
Sabina Spiga
Publication year - 2018
Publication title -
nanotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.926
H-Index - 203
eISSN - 1361-6528
pISSN - 0957-4484
DOI - 10.1088/1361-6528/aae81c
Subject(s) - neuromorphic engineering , spiking neural network , computer science , memristor , nonlinear system , artificial neural network , biological neural network , resistive random access memory , benchmark (surveying) , spike (software development) , learning rule , electronic circuit , artificial intelligence , electronic engineering , voltage , machine learning , electrical engineering , physics , software engineering , geodesy , quantum mechanics , engineering , geography