Quantum materials known as Mott insulators can “learn” to respond to external stimuli in a way that mimics animal behaviour, say researchers at Rutgers University in the US. The discovery of behaviours such as habituation and sensitization in these non-living systems could lead to new algorithms for artificial intelligence (AI).
Neuromorphic, or brain-inspired, computers aim to mimic the neural systems of living species at the physical level of neurons (brain nerve cells) and synapses (the connections between neurons). Each of the 100 billion neurons in the human brain, for example, receives electrical inputs from some of its neighbours and then “fires” an electrical output to others when the sum of the inputs exceeds a certain threshold. This process, also known as “spiking”, can be reproduced in nanoscale devices such as spintronic oscillators. As well as being potentially much faster and energy efficient than conventional computers, devices based on these neuromorphic principles might be able to learn how to perform new tasks without being directly programmed to accomplish them.
In the new work, researchers led by Subhasish Mandal in the Department of Physics and Astronomy at Rutgers focused on nickel oxide, which is a typical example of a Mott insulating material. When they monitored how the material’s electrical conductivity changes as the concentration of its atomic defects is reversibly modulated using external stimuli such as oxygen, ozone and light, they found that it mimics non-associative learning. This type of learning is one of the most fundamental ways in which living organisms learn, and it helps animals adapt continuously to changing situations.
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