Collective Hysteron Behavior in Nonlinear Fluidic and Electronic Networks

Return-point memory is an emergent phenomenon that can arise from the collective properties of elements in a network. When these elements are bistable, the network can exhibit hysteresis, with its exact behavior depending on how each element transitions between states. The fluid transport behaviors of natural flow networks like animal and plant vasculature may be informed by this property of bistability. While multistability-induced hysteresis has been studied in many canonical mechanical systems, such behavior has proven more difficult to reproduce experimentally in flow networks. Here we present an electronic hysteretic network which can serve as a model system to probe these emergent phenomena in complex flow networks. The tunability of each element's current-voltage characteristic allows us to probe different transition graphs of the Preisach model, and we demonstrate our system's capability to generate arbitrary voltage patterns from its global memory with a carefully-tuned network of 9 elements in series. This tunable nonlinearity can be utilized to engineer more complex networks with unique effects such as avalanches and multiperiodic orbits.