Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains

Entropy

Hassan Nasser¹ 

关键词: neural coding;    Gibbs distribution;    maximum entropy;    convex duality;    spatio-temporal constraints;    large-scale analysis;    spike train;    MEA recordings;   

DOI  :  10.3390/e16042244

来源: mdpi

【 摘 要 】

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], which proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows one to properly handle memory effects in spike statistics, for large-sized neural networks.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland