Although, there are considerable works on the neural mechanisms of reward-based learning and decision making, and most of them mention that addiction can be explained by malfunctioning in these cognitive processes, there are very few computational models. This paper focuses on nicotine addiction, and a computational model for nicotine addiction is proposed based on the neurophysiological basis of addiction. The model compromises different levels ranging from molecular basis to systems level, and it demonstrates three different possible behavioral patterns which are addict, nonaddict, and indecisive. The dynamical behavior of the proposed model is investigated with tools used in analyzing nonlinear dynamical systems, and the relation between the behavioral patterns and the dynamics of the system is discussed. 1. Introduction The value of an experience or an action is imposed by the reward gained afterwards. An action inducing a greater reward is sensed as a superior action, and thus the successive occurrences of this type of actions are rewarded more frequently . In the case of addiction, the abusive substance (nicotine, drugs, etc.) has a value greater than other forms of reward imposing actions. Some persistent modifications in the synaptic plasticity in the neural subsystems of the brain are believed to lead to addiction; thus, it is claimed that addiction is a disorder in the mesolimbic system which develops by the modification of responses to rewarding actions [1–4]. Mislead by the overemphasized reward perception, the addicts compulsively seek for the substance they are addicted to. Since the reward mechanism has been persistently changed, addicts usually cannot be completely cured, and they often relapse into drug use after treatment . Considering the negative social and physical impacts of addiction, any attempt to understand the neural mechanisms underlying this phenomenon is valuable. So, in this study, a computational model of addiction is given. We propose a model for nicotine addiction which is composed of a cortico-striato-thalamic action selection circuit and a dopamine signaling unit operating according to reinforcement learning. The proposed model is based on the interaction of neural structures known to be taking role in addiction and the effect of neurotransmitters on these structures. The proposed model focuses on the neurophysiological basis of addiction and the results obtained by the model show that the model is capable of demonstrating different behavioral patterns. (See the Supplementary Material containing the source codes
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