Bio-based/bio-inspired systems are attracting the interest of many studies even if we are far from reproducing the simplest living cell property. The concept of memory is particularly well suited for mimicking learning behavior in biosystems and in information processing systems being capable of coupling inherently memory and logic capabilities. Bio-electronics is another challenging platform, mostly if we consider organic devices based on conductive and biocompatible polymers. This chapter deals with several examples of devices developed by joining unconventional computing, organic memristors and living being. Starting from organic memristors we realized logic gates with memory and a single layer perceptron. We developed hybrid systems based on living beings as key elements for the proper device working, in particular with Phyarum polycephalum and neurons. These devices enable new and unexplored opportunities in such emerging field of research.

Chapter 18 - Organic Memristor Based Elements for Bio-inspired Computing

Battistoni;Silvia;Alice Dimonte;Victor Erokhin
2017

Abstract

Bio-based/bio-inspired systems are attracting the interest of many studies even if we are far from reproducing the simplest living cell property. The concept of memory is particularly well suited for mimicking learning behavior in biosystems and in information processing systems being capable of coupling inherently memory and logic capabilities. Bio-electronics is another challenging platform, mostly if we consider organic devices based on conductive and biocompatible polymers. This chapter deals with several examples of devices developed by joining unconventional computing, organic memristors and living being. Starting from organic memristors we realized logic gates with memory and a single layer perceptron. We developed hybrid systems based on living beings as key elements for the proper device working, in particular with Phyarum polycephalum and neurons. These devices enable new and unexplored opportunities in such emerging field of research.
2017
Istituto dei Materiali per l'Elettronica ed il Magnetismo - IMEM
978-3-319-33920-7
organic memristor
unconventional computing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/353811
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