Linear-nonlinear-time-warp-poisson models of neural activity

Patrick N. Lawlor*, Matthew G. Perich, Lee E. Miller, Konrad Paul Kording

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

Prominent models of spike trains assume only one source of variability – stochastic (Poisson) spiking – when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

Original languageEnglish (US)
Pages (from-to)173-191
Number of pages19
JournalJournal of Computational Neuroscience
Volume45
Issue number3
DOIs
StatePublished - Dec 1 2018

Keywords

  • Generalized linear model
  • Modeling
  • Poisson process
  • Reaching movements
  • Spike trains

ASJC Scopus subject areas

  • Sensory Systems
  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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