Multi-neuronal activity and functional connectivity in cell assemblies

•Extracting information from multi-neuronal data is much more efficient with statistical models.•These models can be used for inferring functional connectivity in cells assemblies beyond what is offered by pairwise correlation analysis.•Quantifying the role of various sources of correlated activity, for example, common input as well as unrecorded parts of the network is made possible by statistical models.

Our ability to collect large amounts of data from many cells has been paralleled by the development of powerful statistical models for extracting information from this data. Here we discuss how the activity of cell assemblies can be analyzed using these models, focusing on the generalized linear models and the maximum entropy models and describing a number of recent studies that employ these tools for analyzing multi-neuronal activity. We show results from simulations comparing inferred functional connectivity, pairwise correlations and the real synaptic connections in simulated networks demonstrating the power of statistical models in inferring functional connectivity. Further development of network reconstruction techniques based on statistical models should lead to more powerful methods of understanding functional anatomy of cell assemblies.