Neuromorphic MEMS sensor network

Abstract

This work investigates the computational potential of microelectromechanical system (MEMS) networks. In these networks, each MEMS device retains the memory of past inputs through bistability and hysteresis and receives a weighted excitatory or inhibitory feedback from other devices within the network. These interactions are shown to change the dynamics of a small network of MEMS devices to produce selective switching and limit cycles through Hopf bifurcations. Furthermore, we show that interactions within large, trained MEMS networks can be used to perform computational tasks such as object classification and tracking.