Spatio-temporal adaptive penalized splines with application to Neuroscience

TL;DR

This paper introduces a novel three-dimensional spatio-temporal adaptive penalized spline method for modeling neuronal firing rates, utilizing the SOP algorithm for efficient and stable estimation, with applications in neuroscience research.

Contribution

It presents the first statistical approach for locally adaptive smoothing in three dimensions using P-splines and the SOP algorithm.

Findings

First application of 3D adaptive smoothing in neuroscience.

Demonstrates stability and computational efficiency of the SOP algorithm.

Provides improved modeling of neuronal activity data.

Abstract

Data analysed here derive from experiments conducted to study neurons' activity in the visual cortex of behaving monkeys. We consider a spatio-temporal adaptive penalized spline (P-spline) approach for modelling the firing rate of visual neurons. To the best of our knowledge, this is the first attempt in the statistical literature for locally adaptive smoothing in three dimensions. Estimation is based on the Separation of Overlapping Penalties (SOP) algorithm, which provides the stability and speed we look for.