A Distribution Free Unitary Events Method based on Delayed Coincidence Count

TL;DR

This paper introduces a new distribution-free Unitary Events method called Permutation UE, which uses permutation and delayed coincidence counts to detect dependence, controlling FDR with minimal assumptions, and outperforms existing methods in simulations.

Contribution

The paper presents a novel permutation-based Unitary Events method that controls FDR without strong distributional assumptions, improving dependence detection performance.

Findings

Permutation UE controls FDR effectively.

Outperforms trial-shuffling and MTGAUE in simulations.

Demonstrated on real neural data.

Abstract

We investigate several distribution free dependence detection procedures, mainly based on bootstrap principles and their approximation properties. Thanks to this study, we introduce a new distribution free Unitary Events (UE) method, named Permutation UE, which consists in a multiple testing procedure based on permutation and delayed coincidence count. Each involved single test of this procedure achieves the prescribed level, so that the corresponding multiple testing procedure controls the False Discovery Rate (FDR), and this with as few assumptions as possible on the underneath distribution. Some simulations show that this method outperforms the trial-shuffling and the MTGAUE method in terms of single levels and FDR, for a comparable amount of false negatives. Application on real data is also provided.