Introduction to Computational Neuroscience: Modeling Brain Dynamics

How does the brain process information, store memories, and make decisions? Understanding the brain requires looking at it through the lens of mathematics and computation. This text-only course guides you from the fundamental biology of neurons to building computational models of neural systems. You will learn how to simulate single cells, model synaptic connections, and analyze simulated neural activity using modern computational concepts. What you'll learn: - Understand the biophysics of single neurons, starting with the classic Hodgkin-Huxley model. - Model synaptic transmission and network connectivity to simulate basic circuit dynamics. - Apply Python-based computational concepts to simulate neural spiking activity. - Analyze neural data patterns, including spike trains and population coding. - Compare biological neural networks with modern artificial neural network architectures. The course starts with foundational neurobiology and basic differential equations before moving into practical simulation techniques and network models. You will read clear explanations, work through mathematical derivations, and analyze illustrative code snippets. This course is designed for curious beginners, programmers, and students of science or engineering who want to enter the field of computational neuroscience. No prior background in brain biology is required. Begin your journey into the computational mechanics of the mind today.