An engineer-friendly deep dive into how the human brain truly processes information, learns without backpropagation, and uses spiking neural networks, predictive coding, and dopamine-driven TD errors—challenging our notions of AI and matrix math.
An engineer-friendly introduction to Reinforcement Learning in Computational Psychiatry: how Q-learning works, how the brain might implement similar algorithms, and what this means for understanding mental health disorders.
An accessible, engineer-friendly introduction to Predictive Coding: how the brain predicts the future, why prediction errors matter, and what this means for AI, robotics, and intelligent systems.
A practical introduction to the Hierarchical Gaussian Filter (HGF) in computational psychiatry—how it models human perception, belief updating, and learning under uncertainty, with intuitive explanations for software engineers.
A software engineer's deep dive into the core computational models that power modern psychiatry, exploring how code, math, and neuroscience come together to decode the brain's mysteries.
A software engineer's journey through the 2025 Computational Psychiatry Course in Zurich, exploring how computational models and systems thinking can be applied to understanding the human mind and psychiatric research.
