# Estimating smooth and sparse neural receptive fields with a flexible spline basis

arXiv: https://arxiv.org/abs/2108.07537 · HTML: https://mlsanity.com/paper/2108.07537/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 43 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Built on by (most cited first, 1 of 1)

- [Integrable Elasticity via Neural Demand Potentials](https://mlsanity.com/paper/2605.22820/) (2026)

## Abstract (arXiv)

Spatio-temporal receptive field (STRF) models are frequently used to approximate the computation implemented by a sensory neuron. Typically, such STRFs are assumed to be smooth and sparse. Current state-of-the-art approaches for estimating STRFs based on empirical Bayes are often not computationally efficient in high-dimensional settings, as encountered in sensory neuroscience. Here we pursued an alternative approach and encode prior knowledge for estimation of STRFs by choosing a set of basis functions with the desired properties: natural cubic splines. Our method is computationally efficient and can be easily applied to a wide range of existing models. We compared the performance of spline-based methods to non-spline ones on simulated and experimental data, showing that spline-based methods consistently outperform the non-spline versions.