Neural Networks Fundamentals: Computational and Cognitive Models

Discover how biological brain mechanics inspire the algorithms behind modern artificial intelligence. This text-based course guides you through the fundamental principles of neural computation, bridging the gap between cognitive science and computational models. By reading through these lessons, you will build a solid theoretical and practical foundation in neural network architectures. You will understand how biological learning rules translate into mathematical algorithms, preparing you to explore advanced deep learning and AI systems with confidence. What you will learn: Understand the fundamental biology of synaptic connectivity and how it inspires artificial neural networks. Master the mathematics of the perceptron, the foundational building block of neural computing. Explore backpropagation and gradient descent, the core mechanisms behind modern model training. Analyze recurrent networks, dynamical systems, and feedback loops in neural computation. Examine Hebbian learning and unsupervised adaptation principles. Apply these theoretical concepts to simple computational models using clean, written code examples. You will start with essential terminology, learning how biological neurons transmit information, before progressing to mathematical formulations of learning rules. The course concludes with an exploration of recurrent dynamics and modern cognitive modeling applications. Designed entirely for beginners, this course requires no prior background in advanced calculus or neuroscience; basic algebra and curiosity are all you need to start. Start reading today to unlock the core principles of neural computation and build your foundation in AI.