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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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
Issue number3
StatePublished - Dec 1 2018


  • 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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