Detecting assemblies of coordinated neurons with a novel family of maximum entropy models

The last decades have seen significant developments in experimental techniques enabling the simultaneous recording of the activity of thousands of neurons. This opens up the possibility of studying emergent macro-structures in neuronal population activity. However, understanding how to extract robust patterns from such data remains challenging, both due to its high dimensionality and to the relatively small number of datapoints. In this context, recent works have been focusing on detecting groups of neurons with coordinated (or highly correlated) activity, often called cell assemblies.

We propose a novel method based on Bayesian inference of maximum entropy models for detecting and modeling cell assemblies from population activity. The family of statistical models considered is a generalization of the Ising model, with high-order interactions organized in a community-like manner. We call them minimally complex spin models, in reference to their low information theoretic complexity. During this talk, I will explain the method and demonstrate its ability in identifying groups (or “communities’’) of highly correlated variables in artificial data generated from benchmark models with high-order interactions. We will then apply the approach to identify cell assemblies in publicly available data recorded from hundreds of retinal ganglion cells. Finally, we will compare our results with the assemblies obtained from the standard statistical approach based on Principal Component Analysis and discuss some of the advantages offered by our approach.