A Kohonen self-organizing map trains a 12×12 hexagonal grid of neurons on 500 random RGB data points, then animates both the neurons and the mapped data points into a clustered arrangement. The visualization uses d3.js for SVG rendering and transitions, with the kohonen library handling the neural network training. Each neuron’s color is derived from its weight vector via a cubehelix color space, while the data points, initially scattered randomly, smoothly transition to their nearest matching neurons over a four-second animation, revealing how the SOM organizes high-dimensional input into a low-dimensional topological layout.