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

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.

Flexible Pyrene/Phenanthro[9,10‐d]imidazole‐Based Memristive Devices for Mimicking Synaptic Plasticity

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Brain‐Inspired Structural Plasticity through Reweighting and Rewiring in Multi‐Terminal Self‐Organizing Memristive Nanowire Networks

“…Despite a bright future prospective for the development of next‐generation artificial intelligence (AI) systems, it is hard for such rigid top‐down architectures to emulate most typical features of biological neural networks such as high connectivity, adaptability through reconnection and rewiring, and long‐range spatio‐temporal correlation. Alternative ways using unconventional systems consisting of many interacting nano‐parts have been proposed for the realization of biologically plausible architectures where the emergent behavior arises from a complexity similar to that of biological neural circuits . However, these systems were unable to demonstrate bio‐realistic implementation of structural plasticity including reweighting and rewiring and spatio‐temporal processing of input signals similarly to our brain.…”

Photoswitching Molecular Junctions: Platforms and Electrical Properties

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