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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hyman, S.E., Malenka, R.C., Nestler, E.J.: Neural Mechanisms of Addiction: The Role of Reward-Related Learning and Memory. Annual Review of Neuroscience 29, 565–598 (2006)
Spanagel, R., Heilig, M.: Addiction and Its Brain Science. Addiction 100(12), 1813–1822 (2005)
Solomon, R.L., Corbit, J.D.: An Opponent-Process Theory of Motivation. The American Economic Review 68(6), 12–24 (1978)
Koob, G.F., Le Moal, M.: Neurobiological mechanisms for opponent motivational processes in addiction. Phil. Trans. R. Soc. B 363, 3113–3123 (2008)
Dayan, P.: Dopamine, Reinforcement Learning, and Addiction Pharmacopsychiatry. Addiction 42, 56–65 (2009); 100(12), 1813–1822 (2005)
Peele, S., Alexander, B.K.: The Meaning of Addiction Chapter 3: Theories of Addiction, http://www.peele.net/lib/moa3.html
Delgado, M.R.: Reward-Related Resposes in the Human Striatum. Annals of the New York Academy of Sciences 1104, 70–88 (2007)
Gutkin, B.S., Dehaene, S., Changeux, J.P.: A Neurocomputational Hypothesis for Nicotine Addiction. PNAS 103(4), 1106–1111 (2006)
Metin, S., Sengor, N.S.: Dynamical System Approach in Modeling Addiction. Accepted to be published in Proceedings of BICS (2010)
Sengor, N.S., Karabacak, O., Steinmetz, U.: A Computational Model of Cortico-Striato-Thalamic Circuits in Goal-Directed Behavior. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 328–337. Springer, Heidelberg (2008)
Taylor, J.G., Taylor, N.R.: Analysis of Recurrent Cortico-Basal Ganglia-Thalamic Loops for Working Memory. Biological Cybernetics 82, 415–432 (2000)
Gurney, K., Prescott, T.J., Redgrave, P.: A Computational Model of Action Selection in the Basal Ganglia I: A New Functional Anatomy. Biological Cybernetics 84, 401–410 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Metin, S., Serap Şengör, N. (2010). A Neurocomputational Model of Nicotine Addiction Based on Reinforcement Learning. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-15822-3_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15821-6
Online ISBN: 978-3-642-15822-3
eBook Packages: Computer ScienceComputer Science (R0)