A neurocomputational model of nicotine addiction based on reinforcement learning

Selin Metin; Neslihan Serap Şengör

doi:10.1007/978-3-642-15822-3_30

A neurocomputational model of nicotine addiction based on reinforcement learning

Selin Metin^*, Neslihan Serap Şengör

^*Corresponding author for this work

Department of Electronics and Communication Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Continuous exposure to nicotine causes behavioral choice to be modified by dopamine to become rigid, resulting in addiction. In this work, a computational model for nicotine addiction is proposed and the proposed model captures the effect of continuous nicotine exposure in becoming addict through reinforcement learning. The computational model is composed of three subsystems each corresponding to neural substrates taking part in nicotine addiction and these subsystems are realized by nonlinear dynamical systems. Even though the model is sufficient in acquiring addiction, it needs to be further developed to give a better explanation for the process responsible in turning a random choice into a compulsive behavior.

Original language	English
Title of host publication	Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Pages	228-233
Number of pages	6
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-15822-3_30
Publication status	Published - 2010
Event	20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece Duration: 15 Sept 2010 → 18 Sept 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6353 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Artificial Neural Networks, ICANN 2010
Country/Territory	Greece
City	Thessaloniki
Period	15/09/10 → 18/09/10

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-642-15822-3_30

Cite this

Metin, S., & Serap Şengör, N. (2010). A neurocomputational model of nicotine addiction based on reinforcement learning. In Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings (PART 2 ed., pp. 228-233). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6353 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15822-3_30

Metin, Selin ; Serap Şengör, Neslihan. / A neurocomputational model of nicotine addiction based on reinforcement learning. Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 2. ed. 2010. pp. 228-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "A neurocomputational model of nicotine addiction based on reinforcement learning",

abstract = "Continuous exposure to nicotine causes behavioral choice to be modified by dopamine to become rigid, resulting in addiction. In this work, a computational model for nicotine addiction is proposed and the proposed model captures the effect of continuous nicotine exposure in becoming addict through reinforcement learning. The computational model is composed of three subsystems each corresponding to neural substrates taking part in nicotine addiction and these subsystems are realized by nonlinear dynamical systems. Even though the model is sufficient in acquiring addiction, it needs to be further developed to give a better explanation for the process responsible in turning a random choice into a compulsive behavior.",

author = "Selin Metin and {Serap {\c S}eng{\"o}r}, Neslihan",

year = "2010",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Metin, S & Serap Şengör, N 2010, A neurocomputational model of nicotine addiction based on reinforcement learning. in Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6353 LNCS, pp. 228-233, 20th International Conference on Artificial Neural Networks, ICANN 2010, Thessaloniki, Greece, 15/09/10. https://doi.org/10.1007/978-3-642-15822-3_30

A neurocomputational model of nicotine addiction based on reinforcement learning. / Metin, Selin; Serap Şengör, Neslihan.
Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 2. ed. 2010. p. 228-233 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6353 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Continuous exposure to nicotine causes behavioral choice to be modified by dopamine to become rigid, resulting in addiction. In this work, a computational model for nicotine addiction is proposed and the proposed model captures the effect of continuous nicotine exposure in becoming addict through reinforcement learning. The computational model is composed of three subsystems each corresponding to neural substrates taking part in nicotine addiction and these subsystems are realized by nonlinear dynamical systems. Even though the model is sufficient in acquiring addiction, it needs to be further developed to give a better explanation for the process responsible in turning a random choice into a compulsive behavior.

AB - Continuous exposure to nicotine causes behavioral choice to be modified by dopamine to become rigid, resulting in addiction. In this work, a computational model for nicotine addiction is proposed and the proposed model captures the effect of continuous nicotine exposure in becoming addict through reinforcement learning. The computational model is composed of three subsystems each corresponding to neural substrates taking part in nicotine addiction and these subsystems are realized by nonlinear dynamical systems. Even though the model is sufficient in acquiring addiction, it needs to be further developed to give a better explanation for the process responsible in turning a random choice into a compulsive behavior.

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Metin S, Serap Şengör N. A neurocomputational model of nicotine addiction based on reinforcement learning. In Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings. PART 2 ed. 2010. p. 228-233. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-15822-3_30

A neurocomputational model of nicotine addiction based on reinforcement learning

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