From Occasional Choices to Inevitable Musts: A Computational Model of Nicotine Addiction

Author:

Metin Selin1ORCID,Sengor N. Serap1ORCID

Affiliation:

1. Electrical and Electronic Engineering Faculty, Istanbul Technical University, Maslak, Istanbul 34469, Turkey

Abstract

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.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Toward AI-Guided Smoking Cessation: Individualized Nicotine Addiction Modeling Using Gaussian Processes;2023-10-27

2. Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events;Sensors;2020-02-17

3. Decision Tree Model of Smoking Behaviour;2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI);2019-08

4. The reward-attention circuit model: Nicotine’s influence on attentional focus and consequences on attention deficit hyperactivity disorder;Neurocomputing;2017-06

5. A Computational Model of Neural Synchronization in Striatum;Computational Neurology and Psychiatry;2017

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