(RxWiki News) It's mad science. Researchers have found a way to program computers so they can't forget fast enough and begin to show signs of a kind of virtual schizophrenia.
Scientists at The University of Texas at Austin and Yale University have developed a virtual computer model that mimics what happens to the human brain when it has schizophrenia.
The model, what they call a "neural network," acts as though it is releasing too much dopamine so that the network behaves and recalls things in a schizophrenic manner. This model will be used to study the disease more thoroughly.
"Computers can be "trained" to become schizophrenic."
The results of this model strengthen a theory experts call "the hyperlearning hypothesis." This theory suggests that people with schizophrenia can't forget and filter out unimportant details as the normal brain does.
The result is an overload of information - dots connecting or not connecting to create strange, incoherent stories that aren't based on reality.
Folks with schizophrenia lose the ability to focus on what's meaningful in all the stuff (external stimuli, as scientists call them) we collide with everyday.
Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin, explains that in schizophrenia, too much of the hormone dopamine is released. This prevents the brain from distinguishing what's important and not so important.."and the brain ends up learning from things that it shouldn’t be learning from,” Grasemann said.
The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Grasemann and Miikkulainen taught the system a series of simple stories.
The stories were organized into DISCERN’s memory in much the way the human brain stores information - not as distinct units, but as connections of words, sentences, scripts and stories.
In order to model hyperlearning (the inability to forget), Grasemann and Miikkulainen increased the system’s learning rate by essentially telling it to stop forgetting so much. This simulated the human brain having too much dopamine.
“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.”
“Information processing in neural networks tends to be like information processing in the human brain in many ways,” says Grasemann. “So the hope was that it would also break down in similar ways. And it did.”
After its "training," DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.
The parallel between this created neural network and human schizophrenia isn’t absolute proof that the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.
“We have so much more control over neural networks than we could ever have over human subjects,” he says. “The hope is that this kind of modeling will help clinical research.”
Results of this study were published in Biological Psychiatry.