A network-based framework for detecting communities of similar neural spike trains

Recent advances in large-scale electrophysiological recording technologies now allow simultaneous measurement of spike trains from ensembles of neurons. A central challenge in mathematical neuroscience is therefore to identify functional assemblies and their collective organisation directly from this data. The goal of this work is to devise a method to infer the collective organisation of the neurons from their spiking activity. We construct weighted functional networks from neural spike trains using the van Rossum distance to quantify pairwise similarity. The functional network captures similarities in neuron firing patterns and provides a representation of their collective organisation. We hypothesise that similar neuronal assemblies will appear as clustered communities in the network, and employ the Louvain algorithm to detect such assemblies. We validate our approach using synthetic spike train data and simulated data from a stochastic block model of Leaky Integrate and Fire neurons subjected to external Poisson drives, where the ground truth is known. Finally, we apply our approach to large-scale recordings from the Allen Institute Visual Coding: Neuropixels dataset. We find that our methodology works well as long as a sufficient amount of data is available and the temporal structure is strong relative to the noise.