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.
Highlights
Understanding the flow of information in the brain is a key step in determining how the brain processes information
We show that the estimated transfer entropy (TE) values can drastically vary independent of the actual causal relation between the neurons
We use a pairwise comparison method to determine the significance of transfer entropy values in an objective way for spike-train data comprised of multiple trials
Summary
Understanding the flow of information in the brain is a key step in determining how the brain processes information Methods for observing this flow of information include non-invasive methods such as MEG or calcium imaging that produce a pseudo-continuous image of activity in a brain region [1, 2] but lack high spatial resolution. Another approach, multi-unit recording, is an invasive method that inserts electrode arrays into the brain to detect when nearby neurons fire [3, 4], producing a detailed measurement of the activity of a relatively small number of neurons. This data can be processed to produce a list of spike-times for each observed
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