Self-orthogonalizing attractor neural networks emerging from the free energy principle

ArXi:2505.22749v2 Announce Type: replace-cross Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding of neuronal computations and the design of artificial intelligence systems. Here we formalize how attractor networks emerge from the free energy principle applied to a universal partitioning of random dynamical systems.