activity
20032012
most citedHierarchical Clustering Based on Mutual Information

101 citations · 258 across the 6 of their papers we have counts for

collaborators

6 papers

physics.data-an2012

Monitoring spike train synchrony

Thomas Kreuz, Daniel Chicharro, Conor Houghton +2

Recently, the SPIKE-distance has been proposed as a parameter-free and time-scale independent measure of spike train synchrony. This measure is time-resolved since it relies on ins…

physics.bio-ph2010★ 81 cited

Time-resolved and time-scale adaptive measures of spike train synchrony

Thomas Kreuz, Daniel Chicharro, Martin Greschner +1

A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extra…

q-bio.NC2009★ 67 cited

Measuring multiple spike train synchrony

T. Kreuz, D. Chicharro, R. G. Andrzejak +2

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can…

cond-mat.dis-nn2004★ 3 cited

Extracting Phases from Aperiodic Signals

Alexander Kraskov, Thomas Kreuz, Ralph G. Andrzejak +3

We demonstrate by means of a simple example that the arbitrariness of defining a phase from an aperiodic signal is not just an academic problem, but is more serious and fundamental…

q-bio.QM2003★ 101 cited

Hierarchical Clustering Based on Mutual Information

Alexander Kraskov, Harald Stögbauer, Ralph G. Andrzejak +1

Motivation: Clustering is a frequently used concept in variety of bioinformatical applications. We present a new method for hierarchical clustering of data called mutual informatio…

q-bio.QM2003★ 6 cited

Hierarchical Clustering Using Mutual Information

Alexander Kraskov, Harald Stoegbauer, Ralph G. Andrzejak +1

We present a method for hierarchical clustering of data called {\it mutual information clustering} (MIC) algorithm. It uses mutual information (MI) as a similarity measure and expl…