Inferring Neuronal Network Connectivity using Time-constrained Episodes

TL;DR

This paper introduces a data mining approach using time-constrained episode discovery to analyze multi-neuronal spike train data, effectively revealing underlying neuronal connectivity patterns.

Contribution

It presents novel algorithms for discovering frequent episodes with temporal constraints specifically tailored for neuronal spike train analysis.

Findings

Effective in uncovering neuronal connectivity patterns

Algorithms outperform traditional methods in spike train analysis

Demonstrated success through simulation studies

Abstract

Discovering frequent episodes in event sequences is an interesting data mining task. In this paper, we argue that this framework is very effective for analyzing multi-neuronal spike train data. Analyzing spike train data is an important problem in neuroscience though there are no data mining approaches reported for this. Motivated by this application, we introduce different temporal constraints on the occurrences of episodes. We present algorithms for discovering frequent episodes under temporal constraints. Through simulations, we show that our method is very effective for analyzing spike train data for unearthing underlying connectivity patterns.