Probabilistic encoding models for multivariate neural data

Marcus A. Triplett, Geoffrey J. Goodhill

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.

Original languageEnglish
Article number1
JournalFrontiers in Neural Circuits
Volume13
DOIs
StatePublished - Jan 28 2019

Keywords

  • Brain-computer interfaces
  • Calcium imaging
  • Factor analysis
  • Gaussian process
  • Generalized linear model
  • Neural coding
  • Population code

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