title: Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.
abstract: Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory inputs. Artificial neural networks (ANN) progressively extract higher and higher-level features of sensory input and identify the nature of an object based on a priori information. However, some ANNs which use algorithms such as the "deep-dreaming" developed by Google, allow the network to over-emphasize some objects it "thinks" it recognizes in those areas, and iteratively enhance such outputs leading to representations that appear farther and farther from "reality". We suggest that such "deep dreaming" ANNs may model aberrant salience, a mechanism suggested for pathogenesis of psychosis. Such models can generate testable predictions for psychosis.
authors.affiliation: BethIsrael Deaconess Medical Center, United States; Harvard Medical School, United States. Electronic address: mkeshava@bidmc.harvard.edu.
authors.forename: Matcheri S
authors.initials: MS
authors.lastname: Keshavan
authors.affiliation: Cornell University, United States.
authors.forename: Mukund
authors.initials: M
authors.lastname: Sudarshan
mesh_terms: D001921:Brain / Q000503:physiopathology; D003405:Creativity; D006801:Humans; D000069550:Machine Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000503:physiopathology; D011618:Psychotic Disorders / Q000503:physiopathology*; D012559:Schizophrenia / Q000503:physiopathology*
keywords: Artificial neural networks; Deep dreaming; Psychosis; Schizophrenia