A major goal in neuroscience is to understand neural computation in the mammalian cortex. Since the 1950s, we have learnt how cells respond to changes in the environment but the cells have largely been observed one at a time. However, single-cell recording cannot access the complexity of distributed processing and coding in the large, intermixed cell populations of the cortex. To understand this complexity, we need population-wide activity measurements, at single-cell resolution, as well as theoretical models to interpret the data. In this project, we shall combine experiments and theory to enable a paradigmatic shift from single-cell to population analysis for a prototypical high-level cortical system, the navigation system of the mammalian medial entorhinal-hippocampal region. In this system, spatial firing correlates of individual cells are so evident that they have been given simple, descriptive names – such as place cells, grid cells, and head direction cells. The wealth of information on the phenomenology of these cells, and the existence of theoretical frameworks that offer strong predictions on their population-wide activity patterns, renders the system perfect for population-level analyses of cortical computation. We shall introduce experimental tools to obtain the amount and specificity of multi-neuron data required to decipher neural population codes in freely navigating rodents. Guided initially by theory on attractor network dynamics, we shall identify regularities in firing and connectivity patterns of thousands of simultaneously monitored neurons and use the data to test, refine and develop theoretical models. This exercise will be extended to less-understood high-end systems such as lateral entorhinal cortex, where computational operations have remained elusive due to the lack of similar single-cell correlates. The project is transformative in that it will uncover fundamental and general mechanisms of high-end cortical population coding in mammals.