ArticlePloS one2021
Efficient spline regression for neural spiking data.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Characterisation of nocturnal arrhythmia avalanche dynamics: Insights from generalised linear model analysis.Journal of sleep research · 2025Article
- Differential kinematic coding in sensorimotor striatum across behavioral domains reflects different contributions to movement.Nature neuroscience · 2025Article
- Schizophrenia pathology reverse-translated into mouse shows hippocampal hyperactivity, psychosis behaviors and hyper-synchronous events.Molecular psychiatry · 2025Article
- Detecting rhythmic spiking through the power spectra of point process model residuals.Journal of neural engineering · 2024Article
- Detecting rhythmic spiking through the power spectra of point process model residuals.bioRxiv : the preprint server for biology · 2024Article
- Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing.bioRxiv : the preprint server for biology · 2024Article
- Article
- Integrating Statistical and Machine Learning Approaches for Neural Classification.IEEE access : practical innovations, open solutions · 2022Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.