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Computational Models of Addiction

Principal investigator(s): Boris Gutkin, Andrew M. Oster, Mehdi Keramati, (Michael Graupner)

Addiction presents a complex behavioral process whose causes can be postulated on a multiplicity of levels, from molecular and pharmacological to cognitive. Computational approaches to addiction should bridge the neural with the behavioural/cognitive phenomena. Our approach is multi-level . We are striving to synthesize the effects of the drug on the receptor level, neural circuit level and decision making level in order to disentangle the roles of the primary rewarding and hedonic processes of the drug and the opponent processes in the progression from use to addiction.
We focus on nicotine addiction with the overall hypothesis being that self-administration of nicotine results from an abnormally biased learning at the level of the reward and action selection neural circuits. This initial self-administration, when prolonged, leads to the persistent addiction, lasting possibly the life time of the animal/person. The model is developed to reflect the neuroadaptations induced by the drug (nicotine) at the circuit and receptor level, and to marry those with computational models of reinforcement learning. In the initial stage of the project we have proposed a minimal model of nicotine addiction under a simple choice paradigm. We frame the model in terms of actor-critic framework, proposing specific anatomical substrates for both and suggesting a differential drug action. We take into account the changes to neuronal activity induced directly by the drug, in short- and long- term. Such neuroadaptation then lead to pathological action learning, resulting in addictive behavior. The minimal model accounts for a number of experimental findings. This minimal model forms an initial framework for a development of a more comprehensive computational theory of drug addiction. We plan to extend the model to address more complex behavioral situations and to tease apart how the various learning processes are affected by the drug (e.g. learning of reward vs. learning of actions) leading to habitual drug taking. We also plan to consider the role of the prefrontal cortex, in particular executive cognitive control by such over the reward- and action-selection circuits, in addiction.


Keramati, M. and Gutkin, B.S., Imbalanced decision hierarchy in addicts emerging from drug-hijacked dopamine spiraling circuit, PLOS One, 8:4, 1-8 (2013).

Graupner, M., Maex, R., and Gutkin, B.S., Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine, PLoS Computational Biology, in press, (2013).

Wu1, J., Gaol, M., Shen, J., Shi, W., Oster, A.M., and Gutkin, B.S., Cortical control of VTA function and influence on nicotine reward, Biochemical Pharmacology, in press (2013).

Tolu, S., Eddine, R., Marti, F., David, V., Graupner, M., Baudonnat, S.P.M., Besson, M., Reperant, C., Zemdegs, J., Pages, C., Caboche, J., Gutkin, B., Gardier, A.M., Changeux, J., Faure, P., and Maskos, U., Co-activation of VTA DA and GABA neurons mediates nicotine reinforcement, Molecular Psychiatry, in press, (2012).

Zhang, D., Gao, M., Xu, D., Shi, W., Gutkin, B., Steffensen, S., Lukas, R., and Wu, J., Impact of prefrontal cortex in nicotine-induced excitation of VTA dopamine neurons in anesthetized rats, Journal of Neuroscience, in press, (2012).

Keramati, M., Dezfouli, A., and Piray, P., Understanding Addiction as a Pathological State of Multiple Decision Making Processes: A Neurocomputational Perspective, in: Computational Neuroscience of Drug Addiction, eds. Gutkin, B. and Ahmed, S., (2011).

Keramati, M. and Gutkin, B.S., A Reinforcement Learning Theory for Homeostatic Regulation, NIPS, (2011).

Oster, A. and Gutkin, B.S., A reduced model of DA neuronal dynamics that displays quiescence, tonic firing and bursting, J Phyisiol (Paris), in press, (2011).

Gutkin, B.S. and Ahmed, S.H., Computational Neuroscience of Drug Addiction, in: , Springer Series in Computational Neuroscience, Springer Verlag, 10 DOI: 10.1007/978-1-4614-0751-5, (2011).

Graupner, M. and Gutkin, B.S., Modelling Local Circuit Mechanisms for Nicotine Control of Dopamine Activity, in: Computational Neuroscience of Drug Addiction, eds. Gutkin, B.S. and Ahmed, S.H., Computational Neuroscience Series, Springer Verlag, 10, 111-144 (2011).

Piray, P., Keramati, M., Dezfouli, A., Lucas, C., and Mokri, A., Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-like Behavior: A Computational Approach, Neural Computation, 22, 2334-2368 (2010).

Graupner, M. and Gutkin, B., Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms, Acta Pharmacol Sin, 30(6), 681–6 (2009).

Ahmed, S.H., Graupner, M., and Gutkin, B., Computational Approaches to the Neurobiology of Drug Addiction, Pharmacopsychiatry, 42(Suppl. 1), S144-S152 (2009).

Dezfouli, A., Piray, P., Keramati, M., Ekhtiari, H., Lucas, C., and Mokri, A., A Neurocomputational Model for Cocaine Addiction, Neural Computation, 21, 2869-2893 (2009).

Ahmed, S., Bobashev, G., and Gutkin, B.S., The simulation of addiction: pharmacological and neurocomputational models of drug self-administration, Drug Alcohol Depend, 90(2-3), 304-11 (2007).

Bobashev, G., Costenbader, E., and Gutkin, B.S., Comprehensive mathematical modeling in drug addiction sciences, Drug Alcohol Depend, 89(1), 102-6 (2007).

Gutkin, B.S., Dehaene, S., and Changeux, J.P., A neurocomputational hypothesis for nicotine addiction, Proc. Natl. Acad. Sci., 103 (4), 1106-1111 (2006).