Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2016-11-01Online Publication Date
2016-11-01Print Publication Date
2016-05Permanent link to this record
http://hdl.handle.net/10754/622606
Metadata
Show full item recordAbstract
Memristive 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.Citation
Naous 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 name
2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016Additional Links
http://ieeexplore.ieee.org/document/7538988/ae974a485f413a2113503eed53cd6c53
10.1109/ISCAS.2016.7538988