Attractor Landscape

Attractor landscapes describe the energy or stability of different states within a complex system, such as a gene regulatory network or a neural network, with the goal of understanding how the system transitions between these states. Current research focuses on developing robust methods to identify and analyze these landscapes, employing techniques like fuzzy logic, diffeomorphic vector field alignment, and deep reinforcement learning to navigate and control system dynamics, particularly within models like Boolean networks and recurrent neural networks. This work is crucial for understanding diverse phenomena, from cellular reprogramming and disease progression to the optimization capabilities of neural networks, offering insights into the underlying mechanisms governing complex systems.