Mistake gating leads to energy and memory efficient continual learning

[Submitted on 15 Apr 2026 (v1), last revised 14 Jul 2026 (this version, v2)]

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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.

Submission history

From: Mark van Rossum [view email]
[v1] Wed, 15 Apr 2026 18:44:23 UTC (1,365 KB)
[v2] Tue, 14 Jul 2026 13:38:20 UTC (2,476 KB)