Joint sparse coding and temporal dynamics support context reconfiguration
Qianqian Shi, Yue Che, Faqiang Liu, Hongyi Li, Mingkun Xu, Sandra Reinert, Pieter M. Goltstein, Rong Zhao, and Luping Shi

TL;DR
This paper explores how joint sparse coding and temporal dynamics in neural systems enable flexible context switching and memory retention, offering insights into brain function and artificial lifelong learning.
Contribution
It identifies joint sparse coding and temporal dynamics as key mechanisms for context reconfiguration and memory preservation in neural and artificial systems.
Findings
Sparsity reduces cross-context interference.
Temporal dynamics enhance context separability.
Networks with both properties improve lifelong learning retention.
Abstract
Adaptive behavior requires the brain to transition between distinct contexts while maintaining representations of prior experience. The ability to reconfigure neural representations without erasing previously acquired knowledge is central to learning in dynamic environments, yet the neural mechanisms that support this balance remain unclear. Understanding these mechanisms is also critical for addressing catastrophic forgetting in artificial systems designed for lifelong learning. Here, we identify joint sparse coding and temporal dynamics in both the mouse medial prefrontal cortex (mPFC) and computational networks as mechanisms that help preserve prior representations during context transitions. Specifically, sparsity in context-dependent representations reduces cross-context interference, whereas temporal dynamics within the network activity further enhance context separability across…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
