Applying non-parametric testing to discrete transfer entropy

BioRxiv : the Preprint Server for Biology
Wilkie Olin-Ammentorp, Nathaniel Cady

Abstract

Transfer entropy (TE) is a powerful algorithm which attempts to detect the transfer of information from one system to another. In neuroscience, it has the potential to track the movement of information through complex neuronal systems, and provide powerful insights into their organization and operation. One such application is the ability to infer the existence of causal connectivity (such as synaptic pathways) between neurons in a culture being recorded by micro-electrode array (MEA). There are several challenges, however, in applying TE to neurological data; one of these is the ability to robustly classify what experimental TE value qualifies as significant. We find that common methods in spike train analysis such as a Z-test cannot be applied, as their assumptions are not met. Instead, we utilize surrogate data to compute a sample under the null hypothesis (no causal connection), and resample experimental data through Markov chain Monte Carlo (MCMC) methods to create a sample of TE values under experimental conditions. A standard non-parametric test (Mann-Whitney U-test) is then applied to compare these samples, and determine if they represent a significant connection. We have applied this methodology to MEA recordings of ne...Continue Reading

Related Concepts

Microelectrodes
Neurons
Patient Transfer
Laboratory Culture
Health Center
Markov Chain Monte Carlo Methodology
Analysis
Cellular Component Organization
Synaptic Junction Pathway
Parametric Image

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