title: Probabilistic Tensor Decomposition of Neural Population Spiking Activity
abstract: 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 Pólya-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.
authors: Hugo Soulat Sepiedeh Keshavarzi Troy William Margrie Maneesh Sahani
hIndex: Maneesh Sahani 44