A Neurocomputational Model of Nicotine Addiction Based on Reinforcement Learning


Metin S., Şengör N. S.

20th International Conference on Artificial Neural Networks, Thessaloniki, Greece, 15 - 18 September 2010, vol.6353, pp.228-233 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 6353
  • City: Thessaloniki
  • Country: Greece
  • Page Numbers: pp.228-233

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.