It draws on Mathematics, on Philosophy and on Electronics, for theoretical tools, conceptual analysis, and practical technologies. Disciplines such as Linguistics and Psychology have well-established experimental methodologies and conceptual frameworks, for the collection and analysis of data. They provide experimentally derived accounts of the behaviour of these systems, for which they increasingly seek computational, or informational accounts. Computer science provides an armoury of algorithms and models of computation, and the mathematical, statistical and logical frameworks needed to develop and analyse our computational models of natural and artificial systems. Computing technology provides technologies for developing artificial computational systems, and the ability to implement and test computational models of linguistic and cognitive phenomena, by simulation.
Neuroscientists study another complex information-processing system.
Neural network models are simple, abstract, information processing devices, originally inspired by brain architecture, but whose development has been motivated primarily by their ability to learn concepts from examples. Neuroscientists use refinements of neural network models to simulate the behaviour of interacting networks of neurons. Computer scientists view neural networks as one of a family of architectures for machine learning. Use computer simulation and modelling techniques to investigate the behaviour of neurons, and networks of neurons. Understanding the brain, and natural perceptual systems work can suggest novel computational architectures and techniques.