Where Did Convolution Networks Come From? - Evolution from Neo-Cognitron to Modern CNNs

Explore the evolutionary origins of convolutional neural networks through a detailed examination of the Neo-cognitron, an early network architecture that laid the foundation for modern CNNs. Trace the biological inspiration behind CNN design by analyzing Hubel and Wiesel's groundbreaking experiments on the visual cortex, which revealed how simple and complex cells in the brain process visual information. Understand why CNNs use their characteristic layered architecture by examining how biological visual processing systems organize information hierarchically. Discover the computational parallels between simple cells in biology and ReLU activation functions, and learn how complex cells inspired pooling operations. Investigate the mathematical foundations of convolution operations and their role in feature detection, while exploring how the combination of convolution, activation, and pooling layers creates powerful pattern recognition systems. Examine the learning mechanisms that enable these networks to automatically discover relevant features from data, and gain insights into what exactly is being learned at different layers of a convolutional network. The tutorial includes references to seminal papers including Fukushima's original Neo-cognitron research and Hubel and Wiesel's visual cortex experiments, providing a comprehensive historical and technical perspective on the development of one of deep learning's most important architectures.