# Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains

arXiv: https://arxiv.org/abs/1404.3470 · HTML: https://mlsanity.com/paper/1404.3470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2014.

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

- [Inference of stochastic time series with missing data](https://mlsanity.com/paper/2101.11816/) (2021)

## Abstract (arXiv)

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks.