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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0258321 |
| _version_ | 1819274906895908864 |
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| author | Mehrad Sarmashghi Shantanu P Jadhav Uri Eden |
| author_facet | Mehrad Sarmashghi Shantanu P Jadhav Uri Eden |
| author_sort | Mehrad Sarmashghi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-23T23:15:53Z |
| format | Article |
| id | doaj.art-421c6fb4493245adaf4e5522c2781e96 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T23:15:53Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-421c6fb4493245adaf4e5522c2781e962022-12-21T17:26:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011610e025832110.1371/journal.pone.0258321Efficient spline regression for neural spiking data.Mehrad SarmashghiShantanu P JadhavUri EdenPoint 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.https://doi.org/10.1371/journal.pone.0258321 |
| spellingShingle | Mehrad Sarmashghi Shantanu P Jadhav Uri Eden Efficient spline regression for neural spiking data. PLoS ONE |
| title | 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_short | Efficient spline regression for neural spiking data. |
| title_sort | efficient spline regression for neural spiking data |
| url | https://doi.org/10.1371/journal.pone.0258321 |
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