The Neural Engineering Framework (NEF) is a general methodology that allows you to build largescale, biologically plausible, neural models of cognition. In particular, it acts as a neural compiler: you specify the properties of the neurons, the values to be represented, and the functions to be computed, and it solves for the connection weights between components that will perform the desired functions. Importantly, this works not only for feed-forward computations, but recurrent connections as well, allowing for complex dynamical systems including integrators, oscillators, Kalman filters, and so on. It also incorporates realistic local error-driven learning rules, allowing for online adaptation and optimization 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'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.