The basic building blocks of a neural network include neurons, weights, biases, and activation functions. Neurons act as the basic processing units, mimicking biological neurons in the human brain. Each neuron receives input, processes it by applying weights and biases, and then produces an output through an activation function. Layers of neurons (input, hidden, and output layers) work together to learn complex patterns from data. The strength of connections between neurons is adjusted through learning algorithms, allowing the network to improve its predictive capabilities over time, fulfilling tasks such as classification and regression.