support-information-section).

A key function of the brain is to learn about the statistical relationships between events in the world. A mechanism of this learning is associative neural plasticity, controlled by the timing between neural events. Here, we show that experience can dramatically alter the timing rules governing associative plasticity to match the constraints of a particular circuit and behavior, thereby improving learning. In normal mice, the timing requirements for associative plasticity in the oculomotor cerebellum are precisely matched to the 120 ms delay for visual feedback about behavioral errors.1 This task-specific specialization of the timing rules for plasticity is acquired through experience; in dark-reared mice that had never experienced visual feedback about oculomotor errors, plasticity defaulted to a coincidence-based rule. Computational modeling suggests two broad strategies for implementing this Adaptive Tuning of the Timing Rules for Associative Plasticity (ATTRAP), which tune plasticity to different features of the statistics of neural activity. The modeling predicts a critical role of this process in optimizing the accuracy of temporal credit assignment during learning; consistent with this, behavioral experiments revealed a delay in the timing of learned eye movements in mice lacking experience-dependent tuning of the timing rules for plasticity. ATTRAP provides a powerful mechanism for matching the timing contingencies for associative plasticity to the functional requirements of a particular circuit and learning task, thereby providing a candidate neural mechanism for meta-learning.