Title: Inferring Information Flow in Spike-train Data Sets using a Trial-Shuffle Method,
Abstract: 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 obtained 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 by comparing to a created baseline distribution using a rigorous
statistical test. This is in contrast to either globally comparing all neuron
transfer entropy values or comparing pairwise values to a single baseline
value.
In establishing the viability of this method by comparison to several
alternative approaches in the literature, we find evidence that preserving the
inter-spike-interval timing is important.
We then use the trial-shuffle method to investigate information flow within a
model network as we vary model parameters. This includes investigating the
global flow of information within a connectivity network divided into two
well-connected subnetworks, going beyond local transfer of information between
pairs of neurons.