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Inferring information flow in spike-train data sets using a trial-shuffle method

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  • Benjamin L Walker
  • Katherine A Newhall

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.

Suggested Citation

  • Benjamin L Walker & Katherine A Newhall, 2018. "Inferring information flow in spike-train data sets using a trial-shuffle method," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
  • Handle: RePEc:plo:pone00:0206977
    DOI: 10.1371/journal.pone.0206977
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    References listed on IDEAS

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