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.
A method for room temperature demonstration of in-materio reservoir computing (RC) with a single-walled carbon nanotube/porphyrinpolyoxometalate network (SWNT/Por-POM) is proposed. Boolean functions of OR, AND, NOR, NAND, XOR, and XNOR, all were reconstructed with an accuracy >90% via supervised training of linear voltage readouts. The RC pre-requisite of echo-state property and recurrent connection allowed for consistent performances over multiple test datasets and time-shifted target sequences. Moreover, a non-zero machine intelligence index confirmed the presence of negative differential resistance dynamics, incorporating in SWNT/Por-POM the mathematical equivalence of additive and subtractive functions, thereby aiding the construction of such complex Boolean functions.
The need for highly energy-efficient information processing has sparked a new age of material-based computational devices. Among these, random networks of carbon nanotubes (CNTs) complexed with other materials have been extensively investigated owing to their extraordinary characteristics. However, the heterogeneity of carbon nanotube (CNT) research has made it quite challenging to comprehend the necessary features of in-materio computing in a random network of CNTs. Herein, we systematically tackle the topic by reviewing the progress of CNT applications, from the discovery of individual CNT conduction to their recent uses in neuromorphic and unconventional (reservoir) computing. This review catalogues the extraordinary abilities of random CNT networks and their complexes used to conduct nonlinear in-materio computing tasks as well as classification tasks that may replace current energy-inefficient systems.
For scientists in numerous fields, creating a physical device that can function like the human brain is an aspiration. It is believed that we may achieve brain-like spatiotemporal information processing...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.