Invited: Analog Computation with Oscillatory Neural Networks

Dynamical systems exhibit rich and intricate behaviors that can be harnessed for physical computation. Physical computing draws inspiration from complex systems that continuously adapt, self-organize, and minimize energy as they evolve toward stable configurations, naturally enabling parallel processing. These characteristics show promise for tackling difficult scientific challenges, including NP-hard combinatorial optimization problems. However, designing dynamical systems for computation remains challenging, particularly in choosing appropriate technologies and developing scalable circuit implementations. This invited talk will provide an overview of circuit-level implementations of physical computing using coupled oscillatory neural networks (ONNs).