Multi-Scale Brain Modeling: From Neurons to Networks

How does the microscopic activity of single neurons translate into complex human behavior? Understanding the brain requires looking at it as a complex system operating across multiple spatial and temporal scales. This text-based course guides you through the foundational concepts of multi-scale brain modeling, helping you bridge the gap between molecular neuroscience and system-level brain networks. You will explore how researchers integrate diverse datasets to build predictive models of brain function and pathology. In this course, you will: 1. Understand the core principles of neuroscience across spatial scales, from synapses to whole-brain networks. 2. Explore computational models used to simulate neural activity at different levels of complexity. 3. Analyze how modern data integration techniques link microscopic structures with macroscopic brain scans. 4. Learn about modern neuroinformatics tools and open-access brain simulation platforms. 5. Discover how machine learning is applied to analyze large-scale neural datasets. 6. Practice interpreting multi-scale brain data through structured, step-by-step written exercises. Your learning journey begins with key terminology and foundational concepts of neuroanatomy before moving into the mathematical and computational models of neural systems. The course concludes with practical guidance on using open datasets to conceptualize your own multi-scale brain theories. This course is designed for beginners, students, and curious learners who want to understand computational neuroscience without needing prior advanced training in medicine or programming. Begin your journey into the fascinating world of multi-scale brain modeling today.