Neural networks in artificial intelligence (AI) simulate the human brain by mimicking its structure and function. Just as the human brain consists of neurons connected by synapses, artificial neural networks (ANNs) are composed of nodes (artificial neurons) connected by weighted links.

In the human brain, neurons receive input signals through dendrites, process them in the cell body, and transmit output signals through axons to other neurons. Similarly, in ANNs, nodes receive input values, apply a mathematical transformation (usually involving a weighted sum and an activation function), and pass the result to subsequent nodes.

The architecture of ANNs includes multiple layers: an input layer, one or more hidden layers, and an output layer. This structure allows for complex processing and learning. During training, the network adjusts the weights of the connections based on the error of its predictions, a process inspired by the brain’s ability to strengthen or weaken synaptic connections through learning and experience.

Deep learning, a subset of machine learning, involves ANNs with many hidden layers (deep neural networks) that can automatically extract and learn intricate patterns from large datasets. This hierarchical learning process enables AI systems to perform tasks such as image recognition, natural language processing, and decision-making, akin to human cognitive functions.