Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning

Most conventional memristor-based Pavlov associative memory neural network circuits have been working on realizing the learning and forgetting functions of simultaneous conditioning. However, the time interval between unconditional stimulus and conditional stimulus is a critical variable in classica...

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Autores principales:	Xinyu Xu, Weilin Xu, Baolin Wei, Fangrong Hu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Associative memory memristive neural network circuit memristor delay conditioning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/f89da722d53247b0aca6d683e1d437ea
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id	oai:doaj.org-article:f89da722d53247b0aca6d683e1d437ea
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spelling	oai:doaj.org-article:f89da722d53247b0aca6d683e1d437ea2021-11-18T00:09:36ZMemristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning2169-353610.1109/ACCESS.2021.3122973https://doaj.org/article/f89da722d53247b0aca6d683e1d437ea2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591231/https://doaj.org/toc/2169-3536Most conventional memristor-based Pavlov associative memory neural network circuits have been working on realizing the learning and forgetting functions of simultaneous conditioning. However, the time interval between unconditional stimulus and conditional stimulus is a critical variable in classical conditioning. Different unconditional and conditional stimulus intervals evoke associative memory of brains with different rates. For example, learning in simultaneous conditioning is less effective than delay conditioning. Therefore, a memristor-based neural network circuit of delay and simultaneous conditioning is designed. The proposed circuit consists of learning states detection module, voltage control module, and synapse module. Many functions, such as short-delay conditioning learning, long-delay conditioning learning, simultaneous conditioning learning, experience learning, and two types of forgetting are implemented by the circuit. In particular, the so-called experience learning is that learning the forgotten knowledge will be faster than before and learning again will forget more slowly. This function is more bionic. The correctness of the design is demonstrated through simulations using PSPICE.Xinyu XuWeilin XuBaolin WeiFangrong HuIEEEarticleAssociative memorymemristive neural network circuitmemristordelay conditioningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148933-148947 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Associative memory memristive neural network circuit memristor delay conditioning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Associative memory memristive neural network circuit memristor delay conditioning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xinyu Xu Weilin Xu Baolin Wei Fangrong Hu Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
description	Most conventional memristor-based Pavlov associative memory neural network circuits have been working on realizing the learning and forgetting functions of simultaneous conditioning. However, the time interval between unconditional stimulus and conditional stimulus is a critical variable in classical conditioning. Different unconditional and conditional stimulus intervals evoke associative memory of brains with different rates. For example, learning in simultaneous conditioning is less effective than delay conditioning. Therefore, a memristor-based neural network circuit of delay and simultaneous conditioning is designed. The proposed circuit consists of learning states detection module, voltage control module, and synapse module. Many functions, such as short-delay conditioning learning, long-delay conditioning learning, simultaneous conditioning learning, experience learning, and two types of forgetting are implemented by the circuit. In particular, the so-called experience learning is that learning the forgotten knowledge will be faster than before and learning again will forget more slowly. This function is more bionic. The correctness of the design is demonstrated through simulations using PSPICE.
format	article
author	Xinyu Xu Weilin Xu Baolin Wei Fangrong Hu
author_facet	Xinyu Xu Weilin Xu Baolin Wei Fangrong Hu
author_sort	Xinyu Xu
title	Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
title_short	Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
title_full	Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
title_fullStr	Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
title_full_unstemmed	Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning
title_sort	memristor-based neural network circuit of delay and simultaneous conditioning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f89da722d53247b0aca6d683e1d437ea
work_keys_str_mv	AT xinyuxu memristorbasedneuralnetworkcircuitofdelayandsimultaneousconditioning AT weilinxu memristorbasedneuralnetworkcircuitofdelayandsimultaneousconditioning AT baolinwei memristorbasedneuralnetworkcircuitofdelayandsimultaneousconditioning AT fangronghu memristorbasedneuralnetworkcircuitofdelayandsimultaneousconditioning
_version_	1718425262851358720

Memristor-Based Neural Network Circuit of Delay and Simultaneous Conditioning

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