Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism

Cognitive functions in current artificial intelligence networks are tied to the exponential increase in network scale, whereas the human brain can continuously learn hundreds of cognitive functions with remarkably low energy consumption. This advantage partly arises from the brain’s cross-regional temporal development mechanisms, where the progressive formation, reorganization, and pruning of connections from basic to advanced regions, facilitate knowledge transfer and prevent network redundancy. Inspired by these, we propose the Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism(TD-MCL), enabling cognitive enhancement from simple to complex in Perception-Motor-Interaction (PMI) tasks. The model drives sequential evolution of long-range inter-module connections to facilitate positive knowledge transfer, and uses feedback-guided local inhibition/pruning to eliminate prior task redundancies, reducing energy consumption while preserving acquired knowledge. Experiments on the proposed cross-domain PMI dataset and general datasets (CIFAR100, ImageNet) show that the proposed method can achieve continual learning capabilities while reducing network scale, without introducing regularization, replay, or freezing strategies, and achieving superior accuracy on new tasks compared to direct learning. The proposed method shows that the brain’s developmental mechanisms offer a valuable reference for exploring biologically plausible, low-energy enhancements of general cognitive abilities.