New Attractor

Research on new attractors focuses on understanding and manipulating the stable states within dynamical systems, particularly in neural networks and other complex systems. Current efforts concentrate on developing methods to design, control, and identify attractors using various architectures, including reservoir computers, Hopfield networks, and neural ordinary differential equations, often incorporating techniques from dynamical systems theory and machine learning. This research is significant for improving the robustness and performance of machine learning models, enhancing our understanding of complex biological systems like gene regulatory networks, and enabling the design of more reliable and predictable artificial systems.