Neural network architecture refers to the structure and organisation of an artificial neural network, which is a mathematical model inspired by the structure and functioning of the human brain.

Neural network architecture defines how artificial neurons are connected to each other to process and transmit information. Neural networks are composed of layers of interconnected neurons, each of which performs mathematical operations on the input received and produces an output that is sent to the next layer.

Neural network architecture can be simple or complex, depending on the complexity of the problem being addressed. For example, a simple neural network architecture might have only one input layer, one hidden layer and one output layer, while a more complex architecture might have multiple hidden layers and non-linear inter-layer connections.

The choice of neural network architecture is an important aspect of building a deep learning model, and can have a significant impact on its performance and efficiency. Machine learning researchers continue to explore and develop new neural network architectures to address a wide range of problems in areas such as computer vision, natural language processing and speech recognition, among others.