The firing of neural populations is coordinated across cells, in time, and across experimental conditions or repeated experimental trials, and so a full understanding of the computational significance of neural responses must be based on a separation of these different contributions to structured activity. Tensor decomposition is an approach to untangling the influence of multiple factors in data that is common in many fields. However, despite some recent interest in neuroscience, wider applicability of the approach is hampered by the lack of a full probabilistic treatment allowing principled inference of a decomposition from non-Gaussian spike-count data. Here, we extend the Polya-Gamma (PG) augmentation, previously used in sampling-based Bayesian inference, to implement scalable variational inference in non-conjugate spike-count models. Using this new approach, we develop techniques related to automatic relevance determination to infer the most appropriate tensor rank, as well as to incorporate priors based on known brain anatomy such as the segregation of cell response properties by brain area. We apply the model to neural recordings taken under conditions of visual-vestibular sensory integration, revealing how the encoding of self- and visual-motion signals is modulated by the sensory information available to the animal
