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Firing mechanism based on single memristive neuron and double memristive coupled neurons
by
Sun, Jingru
, Wang, Chunhua
, Shen, Hui
, Cai, Shuo
, Yu, Fei
in
Automotive Engineering
/ Classical Mechanics
/ Control
/ Digital electronics
/ Dynamical Systems
/ Engineering
/ Hysteresis loops
/ Mechanical Engineering
/ Memristors
/ Neural networks
/ Neurons
/ Original Paper
/ Vibration
2022
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Firing mechanism based on single memristive neuron and double memristive coupled neurons
by
Sun, Jingru
, Wang, Chunhua
, Shen, Hui
, Cai, Shuo
, Yu, Fei
in
Automotive Engineering
/ Classical Mechanics
/ Control
/ Digital electronics
/ Dynamical Systems
/ Engineering
/ Hysteresis loops
/ Mechanical Engineering
/ Memristors
/ Neural networks
/ Neurons
/ Original Paper
/ Vibration
2022
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Do you wish to request the book?
Firing mechanism based on single memristive neuron and double memristive coupled neurons
by
Sun, Jingru
, Wang, Chunhua
, Shen, Hui
, Cai, Shuo
, Yu, Fei
in
Automotive Engineering
/ Classical Mechanics
/ Control
/ Digital electronics
/ Dynamical Systems
/ Engineering
/ Hysteresis loops
/ Mechanical Engineering
/ Memristors
/ Neural networks
/ Neurons
/ Original Paper
/ Vibration
2022
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Firing mechanism based on single memristive neuron and double memristive coupled neurons
Journal Article
Firing mechanism based on single memristive neuron and double memristive coupled neurons
2022
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Overview
Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects.
Publisher
Springer Netherlands,Springer Nature B.V
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