Mathematical Modeling II: Local neurodynamics and treatment predictions
The overarching goal of this sub-project lies in developing mathematical methods to characterize alterations in local neurodynamics, which may underlie the observed alterations in behavioral patterns in different stages of alcohol addition. In a second step, we want to investigate if and to what extent different pharmacological interventions could restore potential 'healthy modes' of network operation based on these methods. The identification of maladaptive network dynamics in experimental data sets may serve to identify risk profiles, as well as to develop possible new prevention and treatment procedures of alcohol addiction. The developed mathematical methods and models will specifically be tested on experimental data obtained from a probabilistic decision-making task. The task is designed to entail different cognitive and motivational components essential to alcohol addiction such as the ability to delay gratification (inhibition of spontaneous behavioral responses), working memory, and the representation of reward probabilities. In this context, besides developing methods which describe neuronal dynamics, we further establish computational models of behavior which allow to formally describe the involved cognitive and motivational components, on the basis of which a detailed relationship to neuronal processes can be obtained.