A computer network, simulated to mimic an excessive release of dopamine, tended to recall memories in a schizophrenic way, according to researchers at the University of Texas at Austin and Yale University.
“The hypothesis is that dopamine encodes the importance, the salience, of experience,” said Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin.
“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.”
The study reaffirms a hypothesis known as ‘hyper-learning,’ which suggests that people with schizophrenia lose the ability to forget or ignore as much as they normally would.
When a person loses the ability to decipher what’s meaningful from the enormous amounts of stimuli in the brain, they begin making connections that aren’t real, or start drowning in an ocean of so many connections that they cannot put together any kind of coherent story.
The neural network (called DISCERN) was developed by Grasemann’s adviser, Risto Miikkulainen, Ph.D., and is able to learn natural language.
DISCERN was used to simulate what happens to language during eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, M.D., professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.
In order to mimic the process, researchers began teaching DISCERN some simple stories that were then assimilated into DISCERN’s memory in much the same way the human brain stores information: not as separate units, but as statistical relationships of words, sentences, scripts and stories.
“With neural networks, you basically train them by showing them examples, over and over and over again,” said Grasemann.
“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.”
The researchers modeled hyper-learning by running the system through its paces again, but changed one key factor: They mimicked a large release of dopamine by increasing the system’s learning rate – basically telling it to stop forgetting so much.
“It’s an important mechanism to be able to ignore things,” says Grasemann. “What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia.”
Once it was retrained with the elevated learning rate, DISCERN began inserting itself into fantastical, delusional stories that incorporated elements from other stories it had been told to remember. For example, in one instance, DISCERN claimed responsibility for a terrorist bombing.
In another example, DISCERN started showing evidence of “derailment” – replying to requests for a specific memory with a jumble of dissociated sentences, abrupt departures from the subject and constant jumping from the first- to the third-person and back again.
“Information processing in neural networks tends to be like information processing in the human brain in many ways,” said Grasemann. “So the hope was that it would also break down in similar ways. And it did.”
The similarity between the neural network and human schizophrenia isn’t indisputable proof that the hyper-learning hypothesis is correct, said Grasemann. It does, however, offer support for the hypothesis.
“We have so much more control over neural networks than we could ever have over human subjects,” he said. “The hope is that this kind of modeling will help clinical research.”
The study is published in Biological Psychiatry.
Source: University of Texas at Austin