RT Journal Article SR Electronic T1 Linear-Nonlinear-Time-Warp-Poisson models of neural activity JF bioRxiv FD Cold Spring Harbor Laboratory SP 194498 DO 10.1101/194498 A1 Patrick N. Lawlor A1 Matthew G. Perich A1 Lee E. Miller A1 Konrad P. Kording YR 2018 UL http://biorxiv.org/content/early/2018/01/22/194498.abstract AB 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.