A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate

Hirofumi Tanaka,Daisuke Tanaka,Tetsuya Asai,Megumi Akai-Kasaya,Hakaru Tamukoh,Lingxiang Fu,Liu Hong,Amin Termehyousefi,Takuji Ogawa

doi:10.1038/s41467-018-04886-2

Abstract

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuromorphic devices is much less than that of human brains. In this report, we present molecular neuromorphic devices, composed of a dynamic and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). We show experimentally that the SWNT/POM network generates spontaneous spikes and noise. We propose electron-cascading models of the network consisting of heterogeneous molecular junctions that yields results in good agreement with the experimental results. Rudimentary learning ability of the network is illustrated by introducing reservoir computing, which utilises spiking dynamics and a certain degree of network complexity. These results indicate the possibility that complex functional networks can be constructed using molecular devices, and contribute to the development of neuromorphic devices.

Highlights

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities
We present a complexed single-walled carbon nanotubes (SWNTs)/POM network molecular neuromorphic device consisting of a dense and complex network of spiking molecules (PMo12 particles25) that imitates a large-scale spiking neural network
To reveal the possible impulse generation mechanism, we propose an abstract model of the network by assuming a two-dimensional (2D) structure of molecular junctions and demonstrate that the model yields results in good agreement with the experimental results

Summary

Introduction

In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. Based on experimental studies of the fabricated SWNT/POM network, we first discuss its NDR and noise properties and demonstrate its collective impulse generation.

Results

Conclusion