Thomas Kreuz

Institute for complex systems (ISC) CNR, Florence, Italy, Firenze, Toscana, Italy 
 University of California, San Diego, La Jolla, CA 
Computational Neuroscience, Signal Processing, Spike train synchrony

I am a computational neuroscientist with a physics background. My main field of interest is the development and application of methods to quantify the synchronization between two or more electrophysiological signals.

In recent years the focus of my attention has been on discrete signals and measures of spike train synchrony such as the SPIKE-distance. Currently, I concentrate on the development of methods to measure spike train synchrony between neuronal populations.

Before I worked on continuous signals, e.g. intracranial EEGs from epilepsy patients, and the objective was to evaluate the usefulness of synchronization measures for epileptic seizure prediction by means of a statistical validation.

In the past, another part of my work dealt with simulations of neuronal models (Hodgkin-Huxley, Fitzhugh-Nagumo) under the influence of noise. Finally, I did some work on nonlinear dynamics and nonlinear time series analysis.

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Mean distance: 15.45 (cluster 17)
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Kreuz T, Senocrate F, Cecchini G, et al. (2022) Latency correction in sparse neuronal spike trains. Journal of Neuroscience Methods. 381: 109703
Adam I, Cecchini G, Fanelli D, et al. (2020) Inferring network structure and local dynamics from neuronal patterns with quenched disorder Chaos, Solitons & Fractals. 140: 110235
Satuvuori E, Mulansky M, Daffertshofer A, et al. (2018) Using spike train distances to identify the most discriminative neuronal subpopulation. Journal of Neuroscience Methods
Satuvuori E, Kreuz T. (2018) Which spike train distance is most suitable for distinguishing rate and temporal coding? Journal of Neuroscience Methods
Malvestio I, Kreuz T, Andrzejak RG. (2017) Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains. Physical Review. E. 96: 022203
Satuvuori E, Mulansky M, Bozanic N, et al. (2017) Measures of spike train synchrony for data with multiple time scales. Journal of Neuroscience Methods
Kreuz T, Satuvuori E, Pofahl M, et al. (2017) Leaders and followers: quantifying consistency in spatio-temporal propagation patterns New Journal of Physics. 19: 043028
Mulansky M, Kreuz T. (2016) PySpike—A Python library for analyzing spike train synchrony Softwarex. 5: 183-189
Kreuz T, Mulansky M, Bozanic N. (2015) SPIKY: a graphical user interface for monitoring spike train synchrony. Journal of Neurophysiology. 113: 3432-45
Kreuz T, Bozanic N, Mulansky M. (2015) SPIKE-Synchronization: a parameter-free and time-resolved coincidence detector with an intuitive multivariate extension Bmc Neuroscience. 16
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