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

^*Bu çalışma için yazışmadan sorumlu yazar

Elektronik ve Hab.Müh.Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Artificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Sayfalar	228-233
Sayfa sayısı	6
Baskı	PART 2
DOI'lar	https://doi.org/10.1007/978-3-642-15822-3_30
Yayın durumu	Yayınlandı - 2010
Etkinlik	20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece Süre: 15 Eyl 2010 → 18 Eyl 2010

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sayı	PART 2
Hacim	6353 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

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???event.eventtypes.event.conference???	20th International Conference on Artificial Neural Networks, ICANN 2010
Ülke/Bölge	Greece
Şehir	Thessaloniki
Periyot	15/09/10 → 18/09/10

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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); Hac. 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).

@inproceedings{1ed59ab468654cc7ae6da8b498e44a03,

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",

doi = "10.1007/978-3-642-15822-3_30",

language = "English",

isbn = "3642158218",

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

number = "PART 2",

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note = "20th International Conference on Artificial Neural Networks, ICANN 2010 ; Conference date: 15-09-2010 Through 18-09-2010",

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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, hac. 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); Hac. 6353 LNCS, No. PART 2).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

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AU - Metin, Selin

AU - Serap Şengör, Neslihan

PY - 2010

Y1 - 2010

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

M3 - Conference contribution

AN - SCOPUS:78049403194

SN - 3642158218

SN - 9783642158216

T3 - 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. 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

Özet

Yayın serisi

???event.eventtypes.event.conference???

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap