Dynamic organization of visual cortical networks revealed by machine learning applied to massive spiking datasets

Abstract

ABSTRACTComplex cognitive functions in a mammalian brain are distributed across many anatomically and functionally distinct areas and rely on highly dynamic routing of neural activity across the network. While modern electrophysiology methods enable recording of spiking activity from increasingly large neuronal populations at a cellular level, development of probabilistic methods to extract these dynamic inter-area interactions is lagging. Here, we introduce an unsupervised machine learning model that infers dynamic connectivity across the recorded neuronal population from a synchrony of their spiking activity. As opposed to traditional population decoding models that reveal dynamics of the whole population, the model produces cellular-level cell-type specific dynamic functional interactions that are otherwise omitted from analysis. The model is evaluated on ground truth synthetic data and compared to alternative methods to ensure quality and quantification of model predictions. Our strategy incorporates two sequential stages – extraction of static connectivity structure of the network followed by inference of temporal changes of the connection strength. This two-stage architecture enables detailed statistical criteria to be developed to evaluate confidence of the model predictions in comparison with traditional descriptive statistical methods. We applied the model to analyze large-scale in-vivo recordings of spiking activity across mammalian visual cortices. The model enables the discovery of cellular-level dynamic connectivity patterns in local and long-range circuits across the whole visual cortex with temporally varying strength of feedforward and feedback drives during sensory stimulation. Our approach provides a conceptual link between slow brain-wide network dynamics studied with neuroimaging and fast cellular-level dynamics enabled by modern electrophysiology that may help to uncover often overlooked dimensions of the brain code.