Learning Path

Probability and Random Variables → Practical Neural Statistics Project

This learning path guides computational neuroscience students through the statistical methods essential for analyzing neural data. It covers foundational probability and statistics, progresses through regression and generalized linear models, introduces mixed effects and Bayesian approaches, and applies these to common neural data analysis tasks. The path emphasizes practical application and understanding of statistical assumptions in neural contexts.