Trial-Shuffle Method for Inferring Information Transfer in Spike-train Data Sets

Understanding information processing in the brain requires the ability to determine the functional connectivity between the different regions of the brain. We present a method using transfer entropy to extract this flow of information between brain regions from spike-train data commonly taken in neurological experiments. Transfer entropy is a statistical measure based in information theory that attempts to quantify the information flow from one process to another, and has been applied to find connectivity in simulated spike train data. Due to statistical error in the estimator, inferring functional connectivity requires a method for determining significance in the transfer entropy values. We discuss the issues with numerical estimation of transfer entropy and resulting challenges in determining significance before presenting the trial-shuffle method as a viable option. The trial-shuffle method, for spike train data that is split into multiple trials, determines significant transfer entropy values independently for each individual pair of neurons, rather than globally comparing all neuron transfer entropy values. We establish the viability of this method by showing that it performs comparably or better to a previous approach in the literature based on the false positive detection rate. We then investigate the performance of the trial-shuffle method in terms of information flow within a network as we vary model parameters.