Computational endophenotypes in addiction (Fiore et al 2018)

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Accession:239540
"... here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. ..."
Reference:
1 . Fiore VG, Ognibene D, Adinoff B, Gu X (2018) A Multilevel Computational Characterization of Endophenotypes in Addiction eNeuro
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Striatum; Basal ganglia;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Dopamine;
Simulation Environment: MATLAB;
Model Concept(s): Addiction; Learning; Reinforcement Learning;
Implementer(s): Fiore, Vincenzo G. [vincenzo.g.fiore at gmail.com]; Ognibene, Dimitri ;
Search NeuronDB for information about:  Dopamine;
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FioreEtAl2018
neural_model
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readme.txt
                            
This is the readme for the models associated with the paper:

Fiore VG, Ognibene D, Adinoff B, Gu X (2018) A Multilevel Computational Characterization of Endophenotypes in Addiction eNeuro

This model was contributed by VG Fiore .