Learning processes underlying representational drift

Determine which learning processes generate representational drift while preserving memory and behavior in biological or artificial learning systems.

Background

The paper frames representational drift as a change in neural population codes that can occur despite stable behavioral performance. Although the study demonstrates that continual-learning rules such as experience replay, functional distillation, and parameter anchoring produce distinct drift profiles in artificial neural networks, it does not determine which underlying learning processes generally generate drift while maintaining memory and behavior.

This problem concerns the unresolved mechanistic basis of drift, including how ongoing learning, synaptic changes, and memory-preservation mechanisms jointly produce changing internal representations without catastrophic forgetting. It is broader than the paper’s experimentally addressed comparison of continual-learning rules.

References

What remains unclear is which learning processes generate such change while preserving memory and behavior.

— Continual-learning rules shape representational drift  (2608.16141 - Si et al., 17 Aug 2026) in Introduction

Continual learning remains unclear.

— Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements  (2608.17310 - Zheng et al., 18 Aug 2026) in Appendix A.1, paragraph “Continual learning remains unclear.”

Regarding drift across waves and time, our current datasets mix users from different time points within each topic, so we cannot cleanly isolate purely temporal drift in this paper. We view a more fine-grained, time-indexed analysis as important future work.

— Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM  (2609.04738 - Li et al., 4 Sep 2026) in Appendix, Section “Shared Subapace Similarity Analysis”

Learning general-purpose representations that remain stable under long-term changes in neural population composition while supporting accurate decoding therefore remains an open challenge.

— NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings  (2610.02864 - Lyu et al., 2 Oct 2026) in Related Work, subsection “Nonstationarity in chronic neural recordings”

Although we analyze representation drift across days, we do not disentangle changes due to learning and behavioral changes from intrinsic neural changes, nor do we model cross-area interactions or region-specific representation drift.

— NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings  (2610.02864 - Lyu et al., 2 Oct 2026) in Section 6, Discussion