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Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning
Author(s) -
Zhang Hang,
Zeng Hao,
Priimagi Arri,
Ikkala Olli
Publication year - 2020
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201906619
Subject(s) - classical conditioning , biomimetics , stimulus (psychology) , associative property , cognitive science , associative learning , generalization , artificial intelligence , computer science , unconditioned stimulus , conditioning , neuroscience , cognitive psychology , psychology , mathematics , mathematical analysis , statistics , pure mathematics
Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli‐responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as “Pavlovian materials” are described, whose stimuli‐responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus‐triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli‐responsive and shape‐memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever‐more intelligent functions are expected.