Computational Modeling in Brain and Cognitive Sciences

How does the brain process information, and how can we model these complex biological systems using computers? Bridging the gap between neuroscience and artificial intelligence requires a solid grasp of computational modeling. This text-based course introduces you to the core computational methods, mathematical models, and programming concepts used to study intelligence in both biological and artificial systems. You will transition from understanding basic biological structures to reading and writing code that simulates cognitive processes and neural networks. What you will learn: Understand the foundational mathematical models of individual neurons and neural populations; Apply modern Python libraries to analyze neural datasets and cognitive behavioral data; Explore the intersection of biological vision and artificial neural networks; Implement basic reinforcement learning algorithms that mimic decision-making; Analyze cognitive architectures and computational theories of mind; Practice writing clean, documented code to simulate simple neural dynamics and cognitive tasks. The course begins with essential terminology and the biological foundations of computational neuroscience. From there, you will progress through written tutorials, analyzing code snippets that implement classic models of learning, memory, and perception. This course is designed for beginners in computational science, cognitive psychology, or artificial intelligence who want to understand the biological inspiration behind modern algorithms. No advanced mathematical background is required, though basic programming familiarity is helpful. Start exploring the computational principles of mind and brain today.