Foundations of Neuronal Dynamics and Computational Brain Modeling

How does the brain process information, and how can we describe this complex biological activity using mathematical rules? Understanding the dynamics of individual neurons is the key to unlocking the secrets of neural coding and computational neuroscience. This text-only course guides you from the absolute basics of neural biology to analyzing mathematical models of single neurons and networks. You will gain a solid intuitive and mathematical understanding of how neurons generate action potentials, transmit signals, and encode information. What you'll learn: - Understand the biological foundations of neural signaling and the biophysics of cell membranes. - Model single-neuron activity using classic Hodgkin-Huxley equations and simplified integrate-and-fire models. - Analyze neural coding schemes to see how brains represent and transmit sensory information. - Simulate network dynamics and synaptic plasticity to understand how groups of neurons learn and interact. - Explore modern applications of spiking neural networks (SNNs) in contemporary artificial intelligence and neuromorphic computing. The course starts with essential biological definitions and foundational biophysics before moving step-by-step through mathematical formulations, parameter analysis, and network-level dynamics. This course is designed for beginners in computational neuroscience, physics, or computer science, with no advanced biology or complex programming background required. Start reading today to master the mathematical language of the brain.