Modeling neural population data

A fundamental challenge in Neuroscience is to infer the emergent properties of networks of neurons. Our current understanding of neural processing is largely based on the response properties of single cells, but techniques to simultaneously record action potentials from populations of neurons are rapidly advancing. This provides new challenges for probabilistic models to characterize networks and to understand their connectivity as well as computational function. We present an overview of statistical models to describe the activity of simultaneously recorded neurons. These methods allow us to interpret the network activity in terms of underlying circuit structure and give insight into functional connectivity.