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. Our understanding of sensory and cognitive processing - a computational understanding of how people perform cognitive tasks, is just starting to develop. However, even overly simple models of natural systems can inform the development of useful artefacts that provide artificial implementations of natural competences, such as pattern recognition, speech synthesis, or mathematical reasoning. Artificial neural network technology, for example, has become a commonplace tool for learning computational models of naturally occurring data.