🤖 AI Summary
This work addresses the high energy consumption, substantial memory overhead, and limited biological plausibility of conventional artificial neural networks in continual learning, which stem from frequent parameter updates. Inspired by human negativity bias and error-related negativity (ERN) signals observed in neuroscience, the authors propose a novel mechanism termed "memorized error-gated learning," wherein synaptic updates are triggered exclusively upon current or historical classification errors. This approach introduces error gating into continual learning for the first time, requiring no additional hyperparameters. It substantially reduces parameter update frequency by 50%–80% while maintaining competitive model performance, thereby significantly lowering both computational energy demands and reliance on replay buffers. The method exhibits strong biological interpretability and engineering practicality, making it well-suited for both incremental and online continual learning scenarios.
📝 Abstract
Synaptic plasticity is metabolically expensive, yet animals continuously update their internal models without exhausting energy reserves. However, when artificial neural networks are trained, the network parameters are typically updated on every sample that is presented, even if the sample was classified correctly. Inspired by the human negativity bias and error-related negativity, we propose 'memorized mistake-gated learning' -- a biologically plausible plasticity rule where synaptic updates are strictly gated by current and past classification errors. This reduces the number of updates the network needs to make by $50\%\sim80\%$. Mistake gating is particularly well suited in two cases: 1) For incremental learning where new knowledge is acquired on a background of pre-existing knowledge, 2) For online learning scenarios when data needs to be stored for later replay, as mistake-gating reduces storage buffer requirements. The algorithm can be implemented in a few lines of code, adds no hyper-parameters, and comes at negligible computational overhead. Learning on mistakes is an energy efficient and biologically relevant modification to commonly used learning rules that is well suited for continual learning.