A ‘forgetful’ computer could result in a better computer.
Researchers from the U.S. Department of Energy’s Argonne National Laboratory, the Brookhaven National Laboratory, the Massachusetts Institute of Technology, Purdue University and Rutgers University, have conducted a study that combined supercomputer simulation and X-ray characterization of a material that gradually forgets things, which could lead to advanced bio-inspired computing.
“The brain has limited capacity and it can only function efficiently because it is able to forget,” Subramanian Sankaranarayanan, an Argonne nanoscientist and study author, said in a statement. “It’s hard to create a non-living material that shows a pattern resembling a kind of forgetfulness, but the specific material we were working with can actually mimic that kind of behavior.”
The researchers used a quantum perovskite to give a simpler non-biological model of what forgetfulness might look like on an electronic level.
The material showed an adaptive response when protons are repeatedly inserted and removed that resembles the brain’s desensitization to a recurring stimulus.
The scientists either removed or added a proton from the perovskite lattice, where the material’s atomic structure either contracts dramatically or expands to accommodate it in a process called lattice breathing.
When this happens multiple times the material’s behavior evolves so the lattice breathing is reduced and the proton threat no longer causes the material to hyperventilate.
“Eventually, it becomes harder to make the perovskite ‘care’ if we are adding or removing a proton,” Hua Zhou, a physicist involved in characterizing the behavior of the material using X-rays provided by Argonne’s Advanced Photon Source (APS), a DOE Office of Science User Facility, said in a statement.
“It’s like when you get very scared on a water slide the first time you go down but each time after that you have less and less of a reaction.”
After the material responds to the protons that were added and subtracted, its ability to resist an electrical current was be severely affected and allowed the material to be effectively programmed. This allowed a scientist to insert or remove protons to control whether or not the perovskite would allow a current.
“These simulations, which quite closely match the experimental results, are inspiring whole new algorithms to train neural networks to learn,” Zhou said.
The perovskite material and neural network algorithms could lead to more efficient artificial intelligence capable of facial recognition, reasoning and human-like decision-making.
