Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.

ABSTRACT: When analysing high-dimensional time-series datasets, the inference of effective networks has proven to be a valuable modelling technique. This technique produces networks where each target node is associated with a set of source nodes that are capable of providing explanatory power for its dynamics. Multivariate Transfer Entropy (TE) has proven to be a popular and effective tool for inferring these networks. Recently, a continuous-time estimator of TE for event-based data such as spike trains has been developed which, in more efficiently representing event data in terms of inter-event intervals, is significantly more capable of measuring multivariate interactions. The new estimator thus presents an opportunity to more effectively use TE for the inference of effective networks from spike t

SUBMITTER: Shorten DP 

PROVIDER: S-EPMC12582509 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature