Efficient spline regression for neural spiking data.

Point process generalized linear models (GLMs) provide a powerful tool for characterizing the coding properties of neural populations. Spline basis functions are often used in point process GLMs, when the relationship between the spiking and driving signals are nonlinear, but common choices for the...

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Autores principales:	Mehrad Sarmashghi, Shantanu P Jadhav, Uri Eden
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/421c6fb4493245adaf4e5522c2781e96
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id	oai:doaj.org-article:421c6fb4493245adaf4e5522c2781e96
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spelling	oai:doaj.org-article:421c6fb4493245adaf4e5522c2781e962021-12-02T20:13:42ZEfficient spline regression for neural spiking data.1932-620310.1371/journal.pone.0258321https://doaj.org/article/421c6fb4493245adaf4e5522c2781e962021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258321https://doaj.org/toc/1932-6203Point process generalized linear models (GLMs) provide a powerful tool for characterizing the coding properties of neural populations. Spline basis functions are often used in point process GLMs, when the relationship between the spiking and driving signals are nonlinear, but common choices for the structure of these spline bases often lead to loss of statistical power and numerical instability when the signals that influence spiking are bounded above or below. In particular, history dependent spike train models often suffer these issues at times immediately following a previous spike. This can make inferences related to refractoriness and bursting activity more challenging. Here, we propose a modified set of spline basis functions that assumes a flat derivative at the endpoints and show that this limits the uncertainty and numerical issues associated with cardinal splines. We illustrate the application of this modified basis to the problem of simultaneously estimating the place field and history dependent properties of a set of neurons from the CA1 region of rat hippocampus, and compare it with the other commonly used basis functions. We have made code available in MATLAB to implement spike train regression using these modified basis functions.Mehrad SarmashghiShantanu P JadhavUri EdenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258321 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mehrad Sarmashghi Shantanu P Jadhav Uri Eden Efficient spline regression for neural spiking data.
description	Point process generalized linear models (GLMs) provide a powerful tool for characterizing the coding properties of neural populations. Spline basis functions are often used in point process GLMs, when the relationship between the spiking and driving signals are nonlinear, but common choices for the structure of these spline bases often lead to loss of statistical power and numerical instability when the signals that influence spiking are bounded above or below. In particular, history dependent spike train models often suffer these issues at times immediately following a previous spike. This can make inferences related to refractoriness and bursting activity more challenging. Here, we propose a modified set of spline basis functions that assumes a flat derivative at the endpoints and show that this limits the uncertainty and numerical issues associated with cardinal splines. We illustrate the application of this modified basis to the problem of simultaneously estimating the place field and history dependent properties of a set of neurons from the CA1 region of rat hippocampus, and compare it with the other commonly used basis functions. We have made code available in MATLAB to implement spike train regression using these modified basis functions.
format	article
author	Mehrad Sarmashghi Shantanu P Jadhav Uri Eden
author_facet	Mehrad Sarmashghi Shantanu P Jadhav Uri Eden
author_sort	Mehrad Sarmashghi
title	Efficient spline regression for neural spiking data.
title_short	Efficient spline regression for neural spiking data.
title_full	Efficient spline regression for neural spiking data.
title_fullStr	Efficient spline regression for neural spiking data.
title_full_unstemmed	Efficient spline regression for neural spiking data.
title_sort	efficient spline regression for neural spiking data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/421c6fb4493245adaf4e5522c2781e96
work_keys_str_mv	AT mehradsarmashghi efficientsplineregressionforneuralspikingdata AT shantanupjadhav efficientsplineregressionforneuralspikingdata AT urieden efficientsplineregressionforneuralspikingdata
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Efficient spline regression for neural spiking data.

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