Introduction to Neural Networks and Brain-Inspired AI

Understanding how the human brain processes information provides powerful insight into artificial intelligence. This written course bridges the gap between biological neuroscience and artificial neural networks, giving you a clear mental model of how computational intelligence works from the ground up. You will start with essential terminology, exploring how natural neurons function and how early computational models like the perceptron were designed. From there, you will learn how modern network architectures build upon these biological principles to process complex data. What you will learn: Understand the core terminology and mathematical principles behind artificial neural networks; Explore the biological structure of the human brain cortex and memory organization; Analyze the architecture and mechanics of a classic perceptron; Compare biological neural pathways with artificial activation functions and network layers; Learn how modern vector representations store and retrieve information in AI models; Apply foundational concepts to comprehend how modern deep learning models learn from data. The course begins with essential definitions of biological and artificial neurons before guiding you through foundational theory, structural comparisons, and practical concepts in modern AI. This course is designed for complete beginners in artificial intelligence, requiring no prior background in advanced mathematics or programming. Start reading today to build a strong theoretical foundation in brain-inspired computing.