Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

Abstract

Hardware‐based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in‐materio RC) platform consisting of a recurrent network formed by the single‐walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/fγ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one‐hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/fγ power law of maximized information processing.

Keywords

• artificial intelligence
• object classification
• physical reservoirs
• recurrent neural network
• waveform generation