Researchers used a computer model to initiate symptoms of a virtual schizophrenia in order to shed more light on the inner mechanisms of schizophrenic brains.
Recently, researchers at the University of Texas at Austin and Yale University used a computer model to initiate symptoms of a virtual schizophrenia in order to shed more light on the inner mechanisms of schizophrenic brains. Researchers used a neural network to simulate the release of dopamine in the brain and found that the network recalled memories “in a distinctly schizophrenic-like fashion.”
The hypothesis behind this experiment contends that dopamine, a compound in the brain that encodes the salience (importance) of certain experiences, is released in too much quantity in the brain. This excess of the compound leads to inflated salience, thus causing the brain to gather information from places it should not learn from.
The results of the test support a hypothesis known as the “hyperlearning hypothesis,” which posits that “people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would.” Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters,” consequently making individuals create connections that are not real. Patients can also get caught up in so many different connections that they lose the ability to formulate a logical story.
In order to find out more about this condition, researchers used a program called DISCERN, a neural network capable of learning natural language. DISCERN simulated the effects of different types of neurological dysfunction on language and the results of the simulations were sent to Ralph Hoffman, professor of psychiatry at the Yale School of Medicine. There, he compared the information reported by DISCERN with data gathered from evaluating human schizophrenics.
From there, researchers began teaching a number of simple stories to DISCERN while the program stored them in its memory. After making sure the network learned the stories through the use of continuous examples, they imitated the hyperlearning hypothesis by simulating a disproportionate release of dopamine. The program ran through its paces again using an increased learning rate by virtually ordering the network to stop forgetting so much information.
Using its augmented learning rate, DISCERN began displaying symptoms of schizophrenia by placing itself at the center of elaborate and bizarre stories that included aspects of other stories it was told to recall. DISCERN would even show signs of “derailment” by replying to requests for a specific memory with “a jumble of dissociated sentences, abrupt digressions and constant leaps from the first to the third-person and back again.”
While the correlation between DISCERN and human schizophrenia does not prove the hyperlearning hypothesis to be correct, the information is still valuable. The results generated by DISCERN support the hypothesis and illustrate that comprehending the human brain can be assisted greatly by the use of neural networks.
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