Two Pillars of Computational Neuroscience: Data Analysis and Simulation
Computational neuroscientists divide their work loosely into two intertwined activities: data analysis of real brain recordings and simulations of mechanistic models, often performed by the same people on different projects.
Learning to Code Through Algorithmic Practice, Not Just Syntax
Aspiring computational neuroscientists and scientific programmers often get stuck on language choice and syntax drills instead of practicing the deeper skill that matters: algorithmic thinking.
Textbook and Paper Roadmap for Computational Neuroscience
Self-studiers entering computational neuroscience need both broad conceptual grounding in brain function and exposure to modeling and analysis texts, plus a habit of reading primary research papers.
Math Prerequisites Without Getting Lost in Abstract Textbooks
Many beginners in computational neuroscience over-focus on mastering vast amounts of abstract math before touching real problems, only to burn out on analysis textbooks and never reach applications.
Project-Based Learning and the Goldilocks Rule for Self-Study
Self-studiers in computational neuroscience progress fastest when they work on small, meaningful projects with the right difficulty—neither trivial nor overwhelming—rather than only consuming lectures or reading.