The Architecture of Learning: From Statistics to Intelligence (little-book-of.github.io)

The piece traces how a biological insight — neurons as discrete signal-processing units — became the mathematical backbone of modern AI. Cajal’s neuron doctrine recast the brain as a network of simple integrators; McCulloch and Pitts formalized that idea into threshold logic units that can implement Boolean functions. Rosenblatt’s perceptron added learning: weights w_i are adjusted by w_i ← w_i + η(t − y)x_i, showing a machine could improve from error (and later motivating work overcoming single-layer limits like XOR). Parallel biological theories, notably Hebb’s “cells that fire together, wire together,” connected synaptic change to memory and inspired associative learning rules. Technically, the narrative ties five crucial ideas that underpin contemporary deep learning: nonlinearity (step, sigmoid, tanh, ReLU) which turns networks into universal approximators; hierarchical composition f(x)=f_n(…f_1(x)) that builds abstractions layer by layer; gradient descent and backpropagation w_new = w_old − η∂L/∂w that allocate credit through layers; and sparse coding for efficient, robust representations. Together they show intelligence as emergent structure — not magic but iterative adjustment of connections — explaining why deep, nonlinear, trainable architectures succeed and guiding ongoing research into efficiency, interpretability, and biologically inspired learning rules.