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Computational Neuroscience of hallucinations

Principal investigator(s): Sophie Deneve, Pantelis Leptourgos and Renaud Jardri

Recent advances in theoretical neuroscience have provided new insights into information processing within large brain-like networks operating in an uncertain world. The computational framework can overcome some of the complexity within the object of study by predicting how basic changes in neural architecture may lead to systems-level changes that translate into changes in behavior. Computational models offer ways to unify basic neurochemical findings with data from more macroscopic levels and to start to apply these findings to cognitive sciences and psychiatry. We are currently developping a theory on how impaired inhibition in hierarchical neural could cause false perceptions. In collaboration with R. Jardri at CHU Lille, we test the predictions of this model with psychophysics tasks in schizophrenic patients and controls.


Jardri, R., Duverne, S., Litvinova, A. S., & Denève, S. (2017). Experimental evidence for circular inference in schizophrenia. Nature Communications, 8, 14218. https://doi.org/10.1038/ncomms14218     
Jardri, R., Hugdahl, K., Hughes, M., Brunelin, J., Waters, F., Alderson-Day, B., … Denève, S. (2016). Are Hallucinations Due to an Imbalance Between Excitatory and Inhibitory Influences on the Brain? Schizophrenia Bulletin, 42(5), 1124–1134. https://doi.org/10.1093/schbul/sbw075 
Deneve, S., & Jardri, R. (2016). Circular inference: Mistaken belief, misplaced trust. Current Opinion in Behavioral Sciences, 11, 40–48. https://doi.org/10.1016/j.cobeha.2016.04.001
Jardri, R., and Deneve, S., Computational models of hallucinations., The Neuroscience of Hallucinations, (2012).