Mistake gating leads to energy and memory efficient continual learning

TL;DR

This paper introduces mistake-gated learning, a biologically inspired method that reduces energy and memory use in continual learning by updating network parameters only when errors occur.

Contribution

The authors propose a simple, hyper-parameter-free mistake gating rule that significantly decreases updates, energy consumption, and storage needs in continual learning scenarios.

Findings

Reduces network updates by 50-80%

Decreases energy and memory requirements in continual learning

Suitable for incremental and online 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.