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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000085


Fractional-ordermemristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit


Author(s):  Yi-fei PU, Bo YU, Qiu-yan HE, Xiao YUAN

Affiliation(s):  College of Computer Science, Sichuan University, Chengdu 610065, China; more

Corresponding email(s):   heqiuyan789@163.com, yuanxiao@scu.edu.cn

Key Words:  Fractional calculus, Fracmemristor, Fracmemristance, Fractional-order memristor, Fractional-order memristive synapses


Yi-fei PU, Bo YU, Qiu-yan HE, Xiao YUAN. Fractional-ordermemristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Yi-fei PU, Bo YU, Qiu-yan HE, Xiao YUAN",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000085"
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%T Fractional-ordermemristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit
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%A Bo YU
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%J Journal of Zhejiang University SCIENCE C
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Abstract: 
This study proposes a novel circuit for the fractional-order memristive neural synaptic weighting. The introduced circuit is different from the majority of the previous integer-order approaches and offers important advantages. Since the concept of memristor is generalized from the classic integer-order memristor to the fractional-order memristor (fracmemristor), a challenging theoretical problem would be whether the fracmemristor can be employed to implement the fractional-order memristive synapses or not. In this research, characteristics of the fractional-order memristive neural synaptic weighting (FMNSW), realized by pulse-based fracmemristor bridge circuit, are investigated. First, the circuit configuration of the FMNSWis explained using a pulse-based fracmemristor bridge circuit. Second, the mathematical proof of the fractional-order learning capability of the FMNSW is analyzed. Finally, experimental work and analyses of the electrical characteristics of the FMNSW are presented. Strong ability of the FMNSW in explaining the cellular mechanisms that underlies learning and memory, which is superior to the traditional integer-order memristive neural synaptic weightings is considered a major advantage for the proposed circuit.

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