Abstract
Brain function emerges from the coordinated activation of neuronal populations known as ensembles. The characterization of ensemble structure and dynamics provides important insights into how brain computations are distributed across neural networks. The proliferation of experimental techniques for recording the activity of neuronal populations calls for a comprehensive statistical method to describe, analyze and characterize these high dimensional datasets. Here we introduce a generative model of synchronous activity to describe spontaneously active neural ensembles. Unlike existing methods, our analysis provides a simultaneous estimation of composition, dynamics and statistical features of these neural populations, including ensemble noise and activity rate. We also introduce ensemble “coherence” as a measure of within-ensemble synchrony. We used our method to characterize population activity throughout the tectum of larval zebrafish, allowing us to make statistical inference on the spatiotemporal organization of tectal ensembles, their composition and the logic of their interactions.