Detecting Neural Assemblies Through Similarity Graph Clustering

A hallmark of neural population activity is neural assemblies, groups of neurons that consistently coactivate. Quantitative analysis of these assemblies requires reliable and objective methods for their detection and extraction from recordings of neural population activity, increasingly in the form of calcium imaging data. Here we discuss an algorithm which achieves this goal. The basic idea of this approach is to form a similarity graph of population activity patterns with a high level of coactivity. Methods developed for community detection in graphs can then be applied to obtain a statistical estimate for the number of assemblies, followed by extraction via standard clustering methods. Expanding on the original MATLAB implementation, here we explain the application of this algorithm to example data using a more recent and efficient Python implementation.