A functional clustering algorithm for the analysis of dynamic network data

TL;DR

This paper introduces a new clustering algorithm for dynamic network data that automatically determines the optimal number of clusters without prior knowledge, demonstrated on neural data to reveal state-dependent patterns.

Contribution

The novel algorithm detects functional clusters in discrete event data without pre-specifying cluster numbers, using surrogate data to determine optimal clustering.

Findings

Outperforms existing methods on simulated neural data

Detects state-dependent clustering in real neural recordings

Reveals neurophysiological processes during sleep and exploration

Abstract

We formulate a novel technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.