Statistical learning and representational drift: A dynamic substrate for memories

Abstract

In many brain areas, neurons exhibit continuous changes in their tuning properties over days, even when supporting stable percepts and behaviors-a phenomenon termed representational drift. How do neuronal circuits maintain stable function when their constituent elements are in constant flux? Here, we review recent theoretical and experimental work on interconnected levels, ranging from perpetual changes in synapses driving drifts in tuning of individual neurons to emergent stability at the population level, preserving similarities of activity patterns associated to specific percepts or behaviors. We propose that statistical learning, beyond its well-established roles during development and adaptation to new contexts, is also essential under steady behavioral and environmental conditions to safeguard the stability of representational similarities. We discuss implications for learning, memory, and forgetting. This framework reconciles the apparent paradox between unstable neural activity and stable perception, suggesting that representations are maintained through dynamic processes rather than static neural codes.