Koppenetal2026 v1.0 — Initial Release This is the first release of Koppen et al 2026, Nature Neuroscience (v1.0), accompanying the manuscript: Neural circuits encode prior knowledge of temporal statistics The brain must infer the state of the world under uncertainty, and Bayesian inference theories propose that this is achieved by combining noisy sensory evidence with prior knowledge shaped by experience. In this work, we provide evidence that cerebellar circuits learn and encode prior distributions of temporal variables during eyeblink conditioning in mice, with these representations reflected in Purkinje cell simple and complex spike signaling. This repository contains the code used to support the analyses and computational modeling in the paper. If you use this code, please cite the associated manuscript.