Open AccessThe Receptron: a device for the implementation of information processing systems based on complex nanostructured systems
Author(s) -
Gianluca Martini,
Matteo Mirigliano,
B. Paroli,
P. Milani
Publication year - 2022
Publication title -
japanese journal of applied physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.487
H-Index - 129
eISSN - 1347-4065
pISSN - 0021-4922
DOI - 10.35848/1347-4065/ac665c
Subject(s) - neuromorphic engineering , computer science , complex system , perceptron , generalization , physical system , set (abstract data type) , artificial intelligence , artificial neural network , theoretical computer science , physics , mathematics , mathematical analysis , quantum mechanics , programming language
Among unconventional computing platforms, neuromorphic artificial systems aim at the reproduction of the human brain functions in terms of classification and pattern recognition capabilities, overcoming the limitations of traditional digital computers and closing the gap with the energetic efficiency of biological systems. Here we present a model, the receptron, based on a generalization of the perceptron, and its physical implementation via a neuromorphic system which opens the way for the exploitation of complex networks of reconfigurable elements. Recently we have reported that nanostructured Au films, fabricated from gold clusters produced in the gas phase, have non-linear and non-local electric conduction properties caused by the extremely high density of grain boundaries and the resulting complex arrangement of nanojunctions. Exploiting these non-linear and non-local properties we produced and tested a receptron that can receive inputs from different electrode configurations and generate a complete set of Boolean functions of n variables for classification tasks. The receptron allows also the classification of non-linearly separable functions without previous training of the device.