Neural networks mimic the human brain through interconnected layers of nodes or neurons that process data. Each neuron receives input, applies a mathematical transformation, and produces an output, analogous to synaptic activity in a biological brain. These layers are arranged in an architecture comprised of an input layer, hidden layers, and an output layer. By adjusting the weights of connections through a process called backpropagation during training, the network learns to recognize patterns and make predictions. This capability allows neural networks to excel in tasks like image recognition, natural language processing, and other complex data-driven applications.