Probabilistic Tensor Decomposition of Neural Population Spiking Activity

Hugo Soulat, Sepiedeh Keshavarzi, Troy William Margrie, Maneesh Sahani

Keywords: Probabilistic, Tensor Decomposition, Neuroscience, Spike, Population Activity, Count

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.

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Code: https://github.com/hugosou/vbgcp