The brain requires surprisingly little energy to adapt to the environment to learn, make ambiguous recognitions, have high recognition capacity and intelligence, and perform complex information processing.
The two main characteristics of neural circuits are “the learning ability of synapses” and “nerve impulses or spikes”. As brain science progresses, the structure of the brain has been gradually clarified, but it is too complicated to imitate completely. Scientists have tried to replicate brain function using simplified neuromorphic circuits and devices that emulate part of the brain’s mechanisms.
By developing neuromorphic chips to artificially mimic the circuits that mimic the structure and function of the brain, the functions of spontaneous spike generation and transmission that mimic nerve impulses (spikes) have not yet been fully utilized.
A joint group of researchers from the Kyushu Institute of Technology and Osaka University studied the control of current rectification in the junctions of various molecules and particles absorbed on single-walled carbon nanotubes (SWNTs) , using conductive atomic force microscopy (C-AFM), and found that negative differential resistance was produced in polyoxometalate (POM) molecules absorbed on SWNT. This suggests that a state of unstable dynamic non-equilibrium occurs in molecular junctions.
In addition, the researchers created extremely dense and random SWNT / POM molecular neuromorphic devices, generating spontaneous spikes similar to the nerve impulses of neurons.
POM is made up of metal atoms and oxygen atoms to form a three-dimensional framework. Unlike ordinary organic molecules, POM can store charges in a single molecule. In this study, negative differential resistance and the generation of lattice spikes were believed to be caused by out-of-equilibrium charge dynamics in the molecular junctions of the lattice.
Thus, the joint research group led by Megumi Akai-Kasaya performed simulation calculations of the random molecular lattice model complexed with POM molecules, capable of storing electrical charges, reproducing the spikes generated from the random molecular lattice. They also demonstrated that this molecular model would most likely become a component of reservoir computing devices. Reservoir computation is planned as next-generation artificial intelligence (AI). The results of their research have been published in Nature Communication.
“The importance of our study is that part of the brain function has been replicated by nano-molecular materials. We have demonstrated the possibility that the random molecular network itself could become neuromorphic AI,” explains the author. principal Hirofumi Tanaka.
The achievements of this group are expected to contribute greatly to the development of neuromorphic devices of the future.
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