Neural ensembles are groups of co-active neurons that may be triggered spontaneously, by sensory stimuli or behavior. Such ensembles are therefore likely to constitute the building blocks of brain function, but little is known about their structure, organization and dynamics.
Studying neural ensembles relies on our ability to identify them. Unlike existing methods, @Giovann82176354 provides a model based approach that uses Bayesian inference techniques to obtain probabilistic estimates of the number, composition and dynamics of neural ensembles.
By applying this method my 2-photon volumetric recordings of spontaneous activity in the optic tectum we recover the structure of multiple spatially compact ensembles spanning multiple imaging planes. (note: this visualization was controlled by a Nintendo 64!)
Ensembles are also extremely dynamic! Here we have coloured neurons based on their ensemble membership to illustrate that there are complex within and between ensemble dynamics, neither have been extensively studied.
Our method provides estimates of the time-points that these ensembles are active allowing for the the interactions between ensembles to be studied. As an example we look at correlations between ensembles and find correlated sub-networks between ensembles in the tectum. 
Therefore this work provides a comprehensive statistical method to that could be used to further integrate the spatiotemporal patterns of neural ensembles across different stimulation, behavioural and disease model paradigms.
Read the preprint at: biorxiv.org/content/early/…
Read the preprint at: biorxiv.org/content/early/…
