Risk-utility tradeoff shapes memory strategies for evolving patterns

TL;DR

This paper presents an energy-based framework for understanding how systems can optimally balance memory utility and risk to effectively classify evolving patterns, with implications for biological and machine learning systems.

Contribution

It introduces a novel analytical framework that models the tradeoff between utility and risk in memory systems handling dynamic stimuli, providing general guidelines for learning in evolving environments.

Findings

Moderate risk tolerance enhances memory robustness.

The framework applies to biological and machine learning systems.

Optimal memory strategies do not require fine-tuning.

Abstract

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.