The Neural Engineering Framework (NEF) is a general methodology that allows the building of large-scale, biologically plausible, neural models of cognition. The NEF acts as a neural compiler: once the properties of the neurons, the values to be represented, and the functions to be computed are specified, it solves for the connection weights between components that will perform the desired functions. Importantly, this works not only for feed-forward computations, but also for recurrent connections, allowing for complex dynamical systems including integrators, oscillators, Kalman filters, etc. The NEF also incorporates realistic local error-driven learning rules, allowing for the online adaptation and optimisation of responses. The NEF has been used to model visual attention, inductive reasoning, reinforcement learning and many other tasks. Recently, we used it to build Spaun, the world\textquoteright s largest functional brain model, using 2.5 million neurons to perform eight different cognitive tasks by interpreting visual input and producing hand-written output via a simulated 6-muscle arm. Our open-source software Nengo was used for all of these, and is available at http://nengo.ca, along with tutorials, demos, and downloadable models.