Delayed switching applied to memristor neural networks | Zendy

Frank Z. Wang | Zendy; Na Helian | Zendy; Sining Wu | Zendy; Xiao Yang | Zendy; Yike Guo | Zendy; Guan Lim | Zendy; Md. Mamunur Rashid | Zendy

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Delayed switching applied to memristor neural networks

Author(s) -

Frank Z. Wang,

Na Helian,

Sining Wu,

Xiao Yang,

Yike Guo,

Guan Lim,

Md. Mamunur Rashid

Publication year - 2012

Publication title -

journal of applied physics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.699

H-Index - 319

eISSN - 1089-7550

pISSN - 0021-8979

DOI - 10.1063/1.3672409

Subject(s) - memristor , hebbian theory , artificial neural network , physical neural network , memistor , computer science , synapse , inertia , switching time , topology (electrical circuits) , control theory (sociology) , physics , electronic engineering , voltage , electrical engineering , neuroscience , control (management) , artificial intelligence , resistive random access memory , engineering , types of artificial neural networks , optoelectronics , recurrent neural network , quantum mechanics , biology

Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the ¡°delayed switching effect.¡± In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons

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