학술논문

Low-Voltage Low-Power Integrable CMOS Circuit Implementation of Integer- and Fractional–Order FitzHugh–Nagumo Neuron Model
Document Type
Periodical
Source
IEEE Transactions on Neural Networks and Learning Systems IEEE Trans. Neural Netw. Learning Syst. Neural Networks and Learning Systems, IEEE Transactions on. 30(7):2108-2122 Jul, 2019
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
General Topics for Engineers
Neurons
Mathematical model
Biological neural networks
Integrated circuit modeling
Field programmable analog arrays
Semiconductor device modeling
Couplings
Analog integrated circuits (ICs)
field-programmable analog array (FPAA) design
FitzHugh–Nagumo (FHN) neuron model
fractional-order networks
hardware neural networks
sinh-domain (SD) design
Language
ISSN
2162-237X
2162-2388
Abstract
The low-voltage low-power sinh-domain (SD) implementations of integer- and fractional-order FitzHugh–Nagumo (FHN) neuron model have been introduced in this paper. Besides, the effect of fractional-orders on the external excitation current and dynamics of the neuron has been examined in this paper. The proposed SD designs of FHN neuron model have the benefits of: 1) low-voltage operation; 2) integrability, due to resistor-less design and the employment of only grounded components; 3) electronic tunability of performance parameters; and 4) low-power implementation due to the inherent properties of SD technique. HSPICE simulator tool and Taiwan Semiconductor Manufacturing Company, Hsinchu, Taiwan 130-nm CMOS process was used to evaluate and verify the correct functioning of the model. In addition, to experimentally verify the operation of the proposed fractional-order FHN neuron model, field-programmable analog array (FPAA) implementation of the model has been presented, and the proper functioning of the model has been verified. To the best of the authors’ knowledge, this is the first paper that describes the electronic realization of the fractional-order FHN neuron model. In addition, it is the first time that the FPAA implementation of any fractional-order neuron model has been presented.