Next Article in Journal
A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity
Next Article in Special Issue
Novel Brain Complexity Measures Based on Information Theory
Previous Article in Journal
The Effect of Scandium Ternary Intergrain Precipitates in Al-Containing High-Entropy Alloys
Previous Article in Special Issue
Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessFeature PaperArticle
Entropy 2018, 20(7), 489; https://doi.org/10.3390/e20070489

A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data

1
Department of Neuroscience, Physiology, and Pharmacology, University College London, London WC1E 6BT, UK
2
Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, USA
3
Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA
*
Author to whom correspondence should be addressed.
Received: 1 May 2018 / Revised: 15 June 2018 / Accepted: 19 June 2018 / Published: 23 June 2018
(This article belongs to the Special Issue Information Theory in Neuroscience)
Full-Text   |   PDF [4389 KB, uploaded 23 June 2018]   |  

Abstract

Correlations in neural activity have been demonstrated to have profound consequences for sensory encoding. To understand how neural populations represent stimulus information, it is therefore necessary to model how pairwise and higher-order spiking correlations between neurons contribute to the collective structure of population-wide spiking patterns. Maximum entropy models are an increasingly popular method for capturing collective neural activity by including successively higher-order interaction terms. However, incorporating higher-order interactions in these models is difficult in practice due to two factors. First, the number of parameters exponentially increases as higher orders are added. Second, because triplet (and higher) spiking events occur infrequently, estimates of higher-order statistics may be contaminated by sampling noise. To address this, we extend previous work on the Reliable Interaction class of models to develop a normalized variant that adaptively identifies the specific pairwise and higher-order moments that can be estimated from a given dataset for a specified confidence level. The resulting “Reliable Moment” model is able to capture cortical-like distributions of population spiking patterns. Finally, we show that, compared with the Reliable Interaction model, the Reliable Moment model infers fewer strong spurious higher-order interactions and is better able to predict the frequencies of previously unobserved spiking patterns. View Full-Text
Keywords: maximum entropy; higher-order correlations; neural population coding; Ising model maximum entropy; higher-order correlations; neural population coding; Ising model
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Cayco-Gajic, N.A.; Zylberberg, J.; Shea-Brown, E. A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data. Entropy 2018, 20, 489.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top