DocumentCode
3318287
Title
Memristor-based synapses and neurons for neuromorphic computing
Author
Le Zheng ; Sangho Shin ; Kang, Sung-Mo Steve
Author_Institution
Jack Baskin Sch. of Eng., Univ. of California, Santa Cruz, Santa Cruz, CA, USA
fYear
2015
fDate
24-27 May 2015
Firstpage
1150
Lastpage
1153
Abstract
A memristor-based architecture for neuromorphic computing is proposed. With memristors mimicking key characteristics of synapses and neurons, such nanoscale neural networks exhibit learning and memory effects with high integration density and scalability. Simulations demonstrate important features including adjustable spike generation, spike-timing and spike-rate dependent plasticity.
Keywords
circuit reliability; memristor circuits; nanoelectronics; neural chips; adjustable spike generation; memristor-based architecture; memristor-based neurons; memristor-based synapses; nanoscale neural networks; neuromorphic computing; spike-rate dependent plasticity; spike-timing; Biological neural networks; Computational modeling; Computer architecture; Memristors; Neuromorphics; Neurons; Timing; STDP; memristor; neuromorphic; neuron; synapse;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems (ISCAS), 2015 IEEE International Symposium on
Conference_Location
Lisbon
Type
conf
DOI
10.1109/ISCAS.2015.7168842
Filename
7168842
Link To Document