An in-silico integration of neurodevelopmental and dopaminergic views of schizophrenia
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917048963039232 |
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| author | Al-Hejji, Xena Duarte, Santina Castro, Jose Guillermo Gomez Contreras, Edgar Bermudez Chalmers, Eric |
| author_facet | Al-Hejji, Xena Duarte, Santina Castro, Jose Guillermo Gomez Contreras, Edgar Bermudez Chalmers, Eric |
| contents | 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 behaviours. These findings point to an interesting potential pathology of schizophrenia: E/I imbalance leads to a compensatory response by the network to increase the excitability of neurons, which increases susceptibility to noise. This suggests that the combination of E/I imbalance and neural noise may be key in the emergence of schizophrenic symptoms. We further notice altered response to reward prediction error in our model, and thus propose that E/I imbalance plus noise can account for both schizophrenia symptoms and dopamine system dysfunction: potentially unifying dopaminergic and neurodevelopmental theories of schizophrenia pathology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An in-silico integration of neurodevelopmental and dopaminergic views of schizophrenia Al-Hejji, Xena Duarte, Santina Castro, Jose Guillermo Gomez Contreras, Edgar Bermudez Chalmers, Eric Neurons and Cognition 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 behaviours. These findings point to an interesting potential pathology of schizophrenia: E/I imbalance leads to a compensatory response by the network to increase the excitability of neurons, which increases susceptibility to noise. This suggests that the combination of E/I imbalance and neural noise may be key in the emergence of schizophrenic symptoms. We further notice altered response to reward prediction error in our model, and thus propose that E/I imbalance plus noise can account for both schizophrenia symptoms and dopamine system dysfunction: potentially unifying dopaminergic and neurodevelopmental theories of schizophrenia pathology. |
| title | An in-silico integration of neurodevelopmental and dopaminergic views of schizophrenia |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2510.24741 |