An in-silico integration of neurodevelopmental and dopaminergic views of schizophrenia
Xena Al-Hejji, Santina Duarte, Jose Guillermo Gomez Castro, Edgar Bermudez Contreras, Eric Chalmers

TL;DR
This study uses a deep reinforcement learning model to simulate schizophrenia, revealing that excitation/inhibition imbalance combined with neural noise may underlie symptoms and dopaminergic dysfunction, unifying neurodevelopmental and dopaminergic theories.
Contribution
It introduces a novel DRL-based model that links E/I imbalance and neural noise to schizophrenia symptoms and dopamine system dysfunction.
Findings
E/I imbalance alone does not produce behavioral changes.
Adding noise induces schizophrenic-like behaviors in the model.
The model suggests a unifying mechanism for schizophrenia pathology.
Abstract
Deep reinforcement learning (DRL) algorithms have the potential to provide new insights into psychiatric disorders. Here we create a DRL model of schizophrenia: a complex psychotic disorder characterized by anhedonia, avoidance, temporal discounting, catatonia, and hallucinations. Schizophrenia's causes are not well understood: dopaminergic theories emphasize dopamine system dysfunction, while neurodevelopmental theories emphasize abnormal connectivity, including excitation/inhibition (E/I) imbalance in the brain. In this study, we suppressed positive (excitatory) connections within an artificial neural network to simulate E/I imbalance. Interestingly, this is insufficient to create behavioral changes; the network simply compensates for the imbalance. But in doing so it becomes more sensitive to noise. Injecting noise into the network then creates a range of schizophrenic-like…
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