Linear-nonlinear-time-warp-poisson Models of Neural Activity

Overview

Journal J Comput Neurosci

Specialties Biology
Neurology

Date 2018 Oct 9

PMID 30294750

Citations 17

Authors

Patrick N Lawlor

Matthew G Perich

Lee E Miller

Konrad P Kording

Affiliations

Soon will be listed here.

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.

Citing Articles

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Sani O, Pesaran B, Shanechi M Nat Neurosci. 2024; 27(10):2033-2045.

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Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.

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Modeling and dissociation of intrinsic and input-driven neural population dynamics underlying behavior.

Vahidi P, Sani O, Shanechi M Proc Natl Acad Sci U S A. 2024; 121(7):e2212887121.

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Dynamical flexible inference of nonlinear latent factors and structures in neural population activity.

Abbaspourazad H, Erturk E, Pesaran B, Shanechi M Nat Biomed Eng. 2023; 8(1):85-108.

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