KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2016-11-01
Print Publication Date2016-05
Permanent link to this recordhttp://hdl.handle.net/10754/622606
MetadataShow full item record
AbstractMemristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.
CitationNaous R, Al-Shedivat M, Neftci E, Cauwenberghs G, Salama KN (2016) Stochastic synaptic plasticity with memristor crossbar arrays. 2016 IEEE International Symposium on Circuits and Systems (ISCAS). Available: http://dx.doi.org/10.1109/ISCAS.2016.7538988.
Conference/Event name2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016