Introduction to Computational Neuroscience: Mathematical Models of the Brain

How does the brain process information, make decisions, and learn? Computational neuroscience bridges the gap between biology and mathematics, allowing us to simulate and understand complex neural systems. Through this course, you will develop a solid theoretical framework to analyze neural coding, signal transmission, and learning dynamics without needing a complex background in biophysics. What you'll learn: - Understand the biological foundations of neurons, ion channels, and synaptic transmission. - Apply mathematical models like the Hodgkin-Huxley equations and cable theory to simulate neural excitability. - Analyze neural coding and information processing using probability and signal detection theory. - Explore reinforcement learning and game theory principles applied to neural decision-making. - Conceptualize neural network dynamics using modern computational logic and Python-friendly mathematical formulations. You will begin with essential terminology and the basic biological foundations of neural activity. From there, you will progress through mathematical modeling of single cells, explore network-level information theory, and study how computational models represent learning and behavior. This course is designed for curious beginners, software developers, and students interested in computational biology. No prior advanced neuroscience or complex mathematics experience is required. Start reading today to unlock the mathematical secrets of the brain.