LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS

The Liquid-Brain Network (LBN) paradigm draws from biological principles of neural plasticity and real-time adaptation to address challenges posed by non-stationary learning environments. Inspired by neurodynamic plasticity and recurrent attention mechanisms, this architecture facilitates lifelong learning, efficient memory representation, and adaptive reasoning. The architecture dynamically encodes temporal patterns through variable neural states, enabling continuous learning and generalization without catastrophic forgetting. This paper proposes a comprehensive framework that integrates time-varying neuron states with recurrent attention modules, enhancing the model’s ability to react, adapt, and store evolving patterns. Benchmarking results highlight LBN's superior performance in energy efficiency, memory retention, and adaptability compared to classical recurrent and spiking models. Furthermore, visual illustrations, graphs, and comparative tables demonstrate its operational advantage for smart autonomous systems.