Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Dou, G; Guo, W; Kong, L; Sun, J; Guo, M; Wen, S

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Dou, G Guo, W Kong, L Sun, J Guo, M Wen, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Trans Biomed Circuits Syst, 2024, PP, (99), pp. 1-11
Issue Date:: 2024-04-15

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Field	Value	Language
dc.contributor.author	Dou, G
dc.contributor.author	Guo, W
dc.contributor.author	Kong, L
dc.contributor.author	Sun, J
dc.contributor.author	Guo, M
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.date.accessioned	2024-08-07T05:32:52Z
dc.date.available	2024-08-07T05:32:52Z
dc.date.issued	2024-04-15
dc.identifier.citation	IEEE Trans Biomed Circuits Syst, 2024, PP, (99), pp. 1-11
dc.identifier.issn	1932-4545
dc.identifier.issn	1940-9990
dc.identifier.uri	http://hdl.handle.net/10453/180325
dc.description.abstract	Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Trans Biomed Circuits Syst
dc.relation.isbasedon	10.1109/TBCAS.2024.3388673
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:32:47Z
pubs.issue	99
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.

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http://hdl.handle.net/10453/180325