# Continual-learning rules shape representational drift

arXiv: https://arxiv.org/abs/2608.16141 · HTML: https://mlsanity.com/paper/2608.16141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2026.

## Built on by (most cited first, 1 of 1)

- [Early Learning Shapes Later Directions Of Representation Change In Continual Learning](https://mlsanity.com/paper/2609.36081/) (2026)

## Abstract (arXiv)

Lifelong learning requires acquiring new knowledge without erasing the old. Yet neural population codes for familiar stimuli and behaviors change over days and weeks. This coexistence of stable memory and changing internal codes may depend on how a learning system prevents forgetting. We therefore tested whether different continual-learning mechanisms produce distinct patterns of representational drift. We trained convolutional networks on sequential image classification tasks and recurrent networks on sequences of cognitive tasks, tracking fixed probe representations across learning. Experience replay preserved earlier tasks in both architectures while representations drifted progressively with the number of intervening tasks. Drift was structured: later visual-processing stages and recurrent units' temporal tuning were especially labile, whereas coarse class organization and task-relevant temporal structure persisted. In contrast, algorithm that strongly anchored weights nearly froze representations. Directly anchoring an old representation during replay likewise suppressed drift and impaired acquisition of subsequent tasks. Together, these results link representational drift to the stability--plasticity trade-off: its magnitude is shaped by the mechanism that protects old knowledge, and suppressing it can restrict future learning. Drift may therefore provide an observable signature of the constraints that enable continual learning in brains and machines.