  Keeping a memory of evolving stimuli is ubiquitous in biology, an example of
which is immune memory for evolving pathogens. However, learning and memory
storage for dynamic patterns still pose challenges in machine learning. Here,
we introduce an analytical energy-based framework to address this problem. By
accounting for the tradeoff between utility in keeping a high-affinity memory
and the risk in forgetting some of the diverse stimuli, we show that a moderate
tolerance for risk enables a repertoire to robustly classify evolving patterns,
without much fine-tuning. Our approach offers a general guideline for learning
and memory storage in systems interacting with diverse and evolving stimuli.

Biological Physics;Populations and Evolution