Introduction to Neural Computation

How does the brain process information, make decisions, and represent the world? Understanding the brain quantitatively is one of the most exciting frontiers in modern science. This text-based course guides you through the fundamental mathematical and computational tools used to model neurons and analyze neural activity. You will transition from understanding basic biological concepts to writing down equations that describe how neurons fire, process sensory inputs, and form networks. What you will learn: Understand the mathematical models of single neurons and how they generate action potentials; Analyze how sensory stimuli are represented and decoded by neural populations; Apply essential quantitative tools such as correlation, convolution, and spectral analysis to neural data; Implement dimensionality reduction techniques like Principal Component Analysis to simplify complex brain activity data; Model simple neural networks and understand their collective computational behavior; Use basic differential equations and linear algebra to simulate neural dynamics. The course begins with foundational concepts in neuroscience and mathematics, ensuring you understand the core terminology before moving on to hands-on computational modeling and data analysis techniques. This program is designed for beginners in computational neuroscience, cognitive science, or data analysis, with no prior advanced biological or mathematical background required. Start exploring the computational mysteries of the mind today.