Bio-Inspired Adaptive Rate Modulation for Multi-task Learning in Progressive Neural Networks - Guettas, Chourouk; Cherif, Foudil (Informatica, 2025)
Sequential multi-task learning faces the fundamental challenge of acquiring new tasks while retaining performance on previously learned tasks. Progressive Neural Networks address catastrophic forgetting through architectural isolation but rely on fixed learning strategies that limit adaptive efficiency. Current adaptive methods typically optimize single factors, missing the coordinated nature observed in biological adaptive systems. Inspired by neuromodulatory systems where four key neurotransmitters (dopamine, serotonin, norepinephrine, and acetylcholine) coordinate learning and adaptation, we propose a Bio-Inspired Adaptive Rate Modulation framework that coordinates four computational modules using organizational principles derived from neuromodulatory systems: reward-based adaptation, stability control, attention gating, and knowledge integration. These modules translate the organizational principles of biological neuromodulation into practical optimization mechanisms for Progressive Neural Networks using standard neural network components. Evaluation on four OpenAI Gymnasium environments demonstrates performance improvements ranging from 1.06% to 155.94% over baseline Progressive Neural Networks, with up to 92% variance reduction and performance retention averaging 98.0% . The framework achieves these gains with reasonable computational overhead. Comprehensive ablation studies confirm each module’s contribution, validating the four-factor design. Results demonstrate that neuromodulation-inspired coordination of multiple adaptive factors significantly outperforms fixed learning strategies, providing a principled approach to adaptive optimization in sequential multi-task learning.