Intrinsic Dynamic

Intrinsic dynamics research focuses on understanding and leveraging the inherent, self-organizing behavior within complex systems, aiming to improve prediction and control. Current efforts concentrate on developing models, such as latent dynamics networks and recurrent spiking neural networks, that capture these dynamics from observational data, often employing techniques like reservoir computing and hypergraph attention mechanisms. This work has significant implications for diverse fields, enhancing the performance of applications ranging from robotics and autonomous driving to financial modeling and video processing through more accurate and generalizable representations of system behavior.