Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned st...

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Main Authors:	Mustafa Khalid, Jun Wu, Taghreed M. Ali, Thaair Ameen, Ahmed A. Moustafa, Qiuguo Zhu, Rong Xiong
Format:	Article
Language:	English
Published:	MDPI AG 2020-07-01
Series:	Brain Sciences
Subjects:	quantum neural network computational modeling classical conditioning lesioned and intact model cortico-hippocampal model
Online Access:	https://www.mdpi.com/2076-3425/10/7/431

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spelling	doaj-c08de1cc9dda4999b34c7e1a4f4bee992020-11-25T03:04:31ZengMDPI AGBrain Sciences2076-34252020-07-011043143110.3390/brainsci10070431Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning ParadigmsMustafa Khalid0Jun Wu1Taghreed M. Ali2Thaair Ameen3Ahmed A. Moustafa4Qiuguo Zhu5Rong Xiong6The State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, ChinaElectrical Engineering Department, University of Baghdad, Baghdad 10071, IraqThe Institute of Computer Science, Zhejiang University, Hangzhou 310027, ChinaThe Marcs Institute for Brain and Behaviour and School of Psychology, Western Sydney University, Sydney 1797, AustraliaThe State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, ChinaThe State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, ChinaMost existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.https://www.mdpi.com/2076-3425/10/7/431quantum neural networkcomputational modelingclassical conditioninglesioned and intact modelcortico-hippocampal model
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Mustafa Khalid Jun Wu Taghreed M. Ali Thaair Ameen Ahmed A. Moustafa Qiuguo Zhu Rong Xiong
spellingShingle	Mustafa Khalid Jun Wu Taghreed M. Ali Thaair Ameen Ahmed A. Moustafa Qiuguo Zhu Rong Xiong Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms Brain Sciences quantum neural network computational modeling classical conditioning lesioned and intact model cortico-hippocampal model
author_facet	Mustafa Khalid Jun Wu Taghreed M. Ali Thaair Ameen Ahmed A. Moustafa Qiuguo Zhu Rong Xiong
author_sort	Mustafa Khalid
title	Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms
title_short	Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms
title_full	Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms
title_fullStr	Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms
title_full_unstemmed	Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms
title_sort	cortico-hippocampal computational modeling using quantum neural networks to simulate classical conditioning paradigms
publisher	MDPI AG
series	Brain Sciences
issn	2076-3425
publishDate	2020-07-01
description	Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.
topic	quantum neural network computational modeling classical conditioning lesioned and intact model cortico-hippocampal model
url	https://www.mdpi.com/2076-3425/10/7/431
work_keys_str_mv	AT mustafakhalid corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT junwu corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT taghreedmali corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT thaairameen corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT ahmedamoustafa corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT qiuguozhu corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms AT rongxiong corticohippocampalcomputationalmodelingusingquantumneuralnetworkstosimulateclassicalconditioningparadigms
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Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

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