Self-learning mechanical circuits

Mechanical adaptive matter is ubiquitous in biological systems, from cytoplasm and biofilms to tissues and flocks. Despite important progress in analyzing the complexity of such structures, understanding how adaptive materials can continually learn and respond to their environment without supervision remains a central challenge. Here, we first construct fundamental mechanical unit operators for adaptive elastic materials and implement one such operator in a physical system. Next, we combine dynamical systems theory with our experimental realizations of adaptive elasticity to understand how elastic objects can be used as computational substrates. In particular, we develop a theoretical framework for studying a class of adaptive elastic materials and demonstrate how directed information propagation within a network leads to learning behavior. By constructing physical realizations of learning adaptive networks, we exhibit the range of computational tasks that such a network can solve. Our results suggest possible routes towards embedding distributed computational power in soft, mechanical materials.