Estimating nonlinear neural response functions using GP priors and kronecker methods

Cristina Savin, Gasper Tkacik

Research output: Contribution to journalArticle

Abstract

Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice. Here we use Gaussian process (GP) priors and exploit recent advances in fast GP inference and learning based on Kronecker methods, to efficiently estimate multidimensional nonlinear tuning functions. Our estimator requires considerably less data than traditional methods and further provides principled uncertainty estimates. We apply these tools to hippocampal recordings during open field exploration and use them to characterize the joint dependence of CA1 responses on the position of the animal and several other variables, including the animal's speed, direction of motion, and network oscillations. Our results provide an unprecedentedly detailed quantification of the tuning of hippocampal neurons. The model's generality suggests that our approach can be used to estimate neural response properties in other brain regions.

Original languageEnglish (US)
Pages (from-to)3610-3618
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - 2016

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Animals
Tuning
Neurons
Brain
Networks (circuits)
Uncertainty

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

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