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Computational Neuropsychiatry group

Tampere University
Photo: Tuomo Mäki-Marttunen

The Computational Neuropsychiatry group develops and employs computational models of brain cells to study the pathophysiology of mental disorders. The methods we use include multicompartmental modelling, modelling of signalling pathways and simulation of neuronal networks, and we also employ tools for gene-expression analysis and statistical genetics to inform our models. A special focus is on developing mechanistic gene-to-phenotype understanding for schizophrenia phenotypes, such as deficits in prepulse inhibition (PPI), mismatch negativity (MMN), altered plasticity of visual evoked potentials (VEP), and alterations in delta-oscillation power. An increased knowledge of the mechanisms of mental disorder phenotypes lays a foundation for improved understanding and, ultimately, treatment of the disease symptoms.


The Computational Neuropsychiatry group is in close collaboration with the Computational Neuroscience group.

External collaborators:


Einevoll lab

Hellgren Kotaleski lab

The Blackwell Computational and Experimental Neuroplasticity Laboratory



ModelPsych: Neural model building for psychiatric diseases - From genes to networks (2020-2026). Funded by Research council of Finland.
SubSchiz: Startle-network modelling for schizophrenia research - insights from subcellular models of neuromodulation (2020-2023). Funded by EBRAINS.
Bioinformatics study of cell membrane transporter proteins in schizophrenia (2024). A seed funding by Tampere University, Faculty of Medicine and Health Technology.

Other members

Previous & associated group members:

Miranda Moore

Praveen Dedigamage

Ahmed Eissa

Ilona Mäkinen