Introduction to Computational Neuroscience: Foundations of Neural Modeling

How does the brain process information, store memories, and make decisions? Computational neuroscience provides the mathematical and computational tools to decode these complex biological processes, bridging the gap between biology and technology. In this text-only course, you will transition from understanding basic biological principles to analyzing mathematical models of neural activity. You will gain a solid foundation in how individual neurons generate electrical signals and how these units network together to perform complex computations. Through clear written explanations and conceptual walkthroughs, you will develop the analytical mindset needed to study the brain as a computational system. What you'll learn: - Understand the foundational biological concepts of neurons, action potentials, and synapses. - Model single-neuron electrical activity using classic mathematical frameworks like the Hodgkin-Huxley model. - Analyze synaptic transmission and plasticity rules, including Hebbian learning and synaptic scaling. - Explore neural network dynamics, population coding, and information processing in the brain. - Examine modern computational approaches, including connections between biological networks and modern artificial neural networks. You will begin with essential biological terminology and fundamental physical principles of cell membranes. From there, you will progress step-by-step through single-cell modeling, synaptic connections, and network-level simulations, ensuring a smooth learning curve. This course is designed for curious beginners, software developers, and students who want to understand the intersection of biology, mathematics, and computation, with no prior background in neuroscience required. Start your journey into understanding the computational power of the brain today.