Bio-inspired systems that leverage self-organization and nonlinear dynamical principles for complex sequential learning. SODE employs attractor–repeller dynamics driven by Hebbian and anti-Hebbian rules, so that temporal structure in an input stream is chunked and represented without an objective function or back-propagation. The framework is robust to imbalanced and hierarchical structure and adapts online as the world changes.