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Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical cond...

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Detalles Bibliográficos
Autores principales: Zeng, Hao, Zhang, Hang, Ikkala, Olli, Priimagi, Arri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cell Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961496/
https://www.ncbi.nlm.nih.gov/pubmed/31984376
http://dx.doi.org/10.1016/j.matt.2019.10.019
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author Zeng, Hao
Zhang, Hang
Ikkala, Olli
Priimagi, Arri
author_facet Zeng, Hao
Zhang, Hang
Ikkala, Olli
Priimagi, Arri
author_sort Zeng, Hao
collection PubMed
description Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks “learn” to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that “learns to walk” under periodic light stimulus, and gripping devices able to “recognize” irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics.
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spelling pubmed-69614962020-01-22 Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators Zeng, Hao Zhang, Hang Ikkala, Olli Priimagi, Arri Matter Article Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks “learn” to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that “learns to walk” under periodic light stimulus, and gripping devices able to “recognize” irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics. Cell Press 2020-01-08 /pmc/articles/PMC6961496/ /pubmed/31984376 http://dx.doi.org/10.1016/j.matt.2019.10.019 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Zeng, Hao
Zhang, Hang
Ikkala, Olli
Priimagi, Arri
Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title_full Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title_fullStr Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title_full_unstemmed Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title_short Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators
title_sort associative learning by classical conditioning in liquid crystal network actuators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6961496/
https://www.ncbi.nlm.nih.gov/pubmed/31984376
http://dx.doi.org/10.1016/j.matt.2019.10.019
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