Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks
| Autor: | Matcheri S. Keshavan, Mukund Sudarshan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Psychosis
media_common.quotation_subject Models Neurological Sensory system 050105 experimental psychology Creativity Machine Learning 03 medical and health sciences 0302 clinical medicine Salience (neuroscience) Perception Neural Pathways medicine Humans 0501 psychology and cognitive sciences Biological Psychiatry media_common Cognitive science Artificial neural network 05 social sciences Brain medicine.disease Psychiatry and Mental health Sensory input Psychotic Disorders Schizophrenia A priori and a posteriori Neural Networks Computer Psychology Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Schizophrenia Research. 188:178-181 |
| ISSN: | 0920-9964 |
| DOI: | 10.1016/j.schres.2017.01.020 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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