Abstract
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.
Highlights
This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form
We will describe existing artificial systems employing associative learning algorithms and in particular two recent synthetic material systems that we coin as Pavlovian materials, since their responses are inspired by the classical conditioning algorithm, a simple form of associative learning (Figure 1e)
We have recently developed a model system consisting of hydrogel networks and gold nanoparticles that follows the classical conditioning algorithm.[93]
Summary
Adaptation is ubiquitous in biological systems, relevant to the present topic. A specific complication is that the word “adaptation” has different denotations in different contexts in biology and technology. Leads to fractures of the constituting polymer chains and the creation of radicals, which further trigger polymerization of the monomers available in the aqueous mixture, leading to mechanical reinforcement of the material Another example deals with gels where structural reorganization takes place during the deformation, suggesting the processes should be denoted in both cases as adaptive mechanics.[48] We point out that, different from the biological/bioinspired viewpoint, in materials science and robotics there exists a wide range of literature where the notion of adaptation, could be interpreted as an alternative for stimuli-responsivity. One important feature of living organisms, partly overlapping with the concepts of responsive materials, is the ability to self-repair, regenerate, or heal wounds.[64] Therein, the system is able to restore its original state after being damaged to maintain its normal functions, such as mobility or insulation In biology, this invariably needs energy dissipation. We could pose an even more simplified question: can artificial materials be developed to algorithmically show these simplified forms of learning? From another perspective, could one program learning algorithmically by selecting a priori an alternative stimulus for a material to trigger the intrinsic response? By algorithmically we mean that if we consider the material system as a black box (as Pavlov originally did with dogs), is it possible that upon feeding suitable stimuli, the material response would follow the logic of biological responses, despite the different underlying signal processing pathways?
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
