Event Abstract Back to Event A neurocomputational model of nicotine addiction based on reinforcement learning Selin Metin1* and Neslihan Serap Sengor1 1 Istanbul Technical University, Türkiye In modeling nicotine addiction, we consider that addiction develops due to malfunctioning of reinforcement learning processes driven by metabolical modifications in the brain. We base our approach on the fact that addiction develops as a result of damaged reward mechanism. Excessive fondness for a substance causes compulsive seeking of that substance and opponent process theory is used in explaining the development of addiction. Nicotine blood level triggers the secretion of dopamine (DA) from the ventral tegmental area (VTA) due to a race with the naturally secreted acetylcholine (Ach) neurotransmitter. In return, glutamate secretion in learning processes is affected and it modifies behavioral choices. Chronic nicotine exposure stamps in the behavioral patterns modified by DA secretion and causes addiction development. The proposed computational model is developed using MATLAB in-house codes and realizes DA secretion from the VTA to the cortico-striato-thalamic (CST) loop using reinforcement learning. DA secretion, action selection, action evaluation, and value assignment subsystems are modeled as nonlinear dynamical systems. The dynamic behavior of these systems is investigated by bifurcation analysis using XPP in order to give an explanation of processes going on from dynamical system point of view. The action selection system uses competitive learning which is modified with VTA DA signaling affected by nicotine. Arithmetically increasing reward value affects the system in favor of selecting the smoking action. Error in expectation symbolizes the modifying effects of the neurotransmitters and changes the output of dorsal striatum, amplifies the emotional input, and updates the stimulus value. Past actions contribute to the evaluation process as the input from the previous action so the cumulative effects of the previous actions trigger the present action selection. During model execution, addictive, non-addictive, and indecisive behaviors are observed in simulation results. Addiction developed in 20/50 trials with an average of 346/1000 steps and standard deviation of 265.76. To improve the proposed model, reward should be computed as a dynamically changing function of model parameters. Also, the approach should be expanded to include the molecular basis of DA secretion mechanism from the VTA to the CST loop involving the relevant neurotransmitters to be able to give a more realistic model. Funding Information: This work is partially supported by BAP-ITU References [1] Gutkin, B.S., Dehaene, S., Changeux, J.P., “A Neurocomputational Hypothesis for Nicotine Addiction”, PNAS, vol.103, no.4, 1106-1111, Jan 24, 2006. [2] Metin, S., Sengor, N.S., A Neurocomputational Model of Nicotine Addiction Based on Reinforcement Learning, ICANN 2010, 20th International Conference on Artificial Neural Networks, (2010), pp: 15-18. Keywords: Dopamine, Neurons, networks and dynamical systems, reinforcement learning Conference: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011. Presentation Type: Poster Topic: neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords) Citation: Metin S and Sengor N (2011). A neurocomputational model of nicotine addiction based on reinforcement learning. Front. Comput. Neurosci. Conference Abstract: BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011. doi: 10.3389/conf.fncom.2011.53.00058 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Aug 2011; Published Online: 04 Oct 2011. * Correspondence: Dr. Selin Metin, Istanbul Technical University, Istanbul, Türkiye, selinmetin@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Selin Metin Neslihan Serap Sengor Google Selin Metin Neslihan Serap Sengor Google Scholar Selin Metin Neslihan Serap Sengor PubMed Selin Metin Neslihan Serap Sengor Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.