  Coupled learning is a contrastive scheme for tuning the properties of
individual elements within a network in order to achieve desired functionality
of the system. It takes advantage of physics both to learn using local rules
and to "compute" the output response to input data, thus enabling the system to
perform decentralized computation without the need for a processor or external
memory. We demonstrate a proof-of-concept mechanical network that can learn
simple tasks such as self-symmetrizing via iterative tuning of individual
spring rest lengths. These mechanical networks could feasibly be scaled and
automated to solve increasingly complex tasks, hinting at a new class of smart
metamaterials.
