Topic: representational similarity kernels

A hallmark of natural intelligence is the ability to sustain stable memories while flexibly learning new associations, allowing animals to adapt to dynamic environments while executing precise behaviors years after they were first learned. Yet, recent experiments have revealed that neural representations of fixed stimuli change continuously over time, contravening the classical assumption that learned features should remain static to maintain task proficiency. The phenomenon of representational drift can be reconciled with normative principles for neural computation within the framework of pro