Comps with schizophrenia to understand human brain
US scientists have afflicted computers with virtual schizophrenia.
Washington: In a unique experiment, scientists in the US claim to have afflicted computers with virtual schizophrenia to better understand the human brain with the condition.
A team from University of Texas and Yale University has used a virtual computer model or "neural network" to simulate the excessive release of dopamine in the brain.
The findings, published in the `Biological Psychiatry` journal, revealed that the network recalled memories in a distinctly schizophrenic-like fashion.
"The hypothesis is that dopamine encodes the importance the salience of experience. When there`s too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn`t be learning from," said team member Uli Grasemann.
The results bolster a hypothesis known in schizophrenia circles 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. They start making connections that aren`t real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.
The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language.
In this research, it was used to simulate what happens to language as result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.
"With neural networks, you basically train them by showing them examples, over and over and over again. Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output.
"You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned," Grasemann said.
In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered.
They simulated an excessive release of dopamine by increasing the system`s learning rate-essentially telling it to stop forgetting so much.
"It`s an important mechanism to be able to ignore things. What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia," said Grasemann.