Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Opponent interactions between serotonin and dopamine
Neural Networks - Computational models of neuromodulation
Reinforcement learning models of the dopamine system and their behavioral implications
Reinforcement learning models of the dopamine system and their behavioral implications
A Computational Model of Cortico-Striato-Thalamic Circuits in Goal-Directed Behaviour
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A neurocomputational model for cocaine addiction
Neural Computation
A neurocomputational model of nicotine addiction based on reinforcement learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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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.