Thomas Kreuz
Affiliations: | Institute for complex systems (ISC) | CNR, Florence, Italy, Firenze, Toscana, Italy | |
| University of California, San Diego, La Jolla, CA |
Area:
Computational Neuroscience, Signal Processing, Spike train synchronyWebsite:
http://www.fi.isc.cnr.it/users/thomas.kreuz/Google:
"https://scholar.google.com/citations?user=x7Dg3pgAAAAJ"Bio:
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) | S | N | B | C | P |
Parents
Sign in to add mentor| Peter Grassberger | grad student | 2000-2003 | Research Centre Juelich, Germany |
| Klaus Lehnertz | grad student | 2000-2003 | University of Bonn, Germany |
| Alessandro Torcini | post-doc | 2004-2006 | CNR |
| Antonio Politi | post-doc | 2004-2007 | CNR, Florence, Italy |
| Henry D I Abarbanel | post-doc | 2007-2009 | UCSD |
Children
Sign in to add trainee| Irene Malvestio | grad student | ||
| Nebojsa Bozanic | grad student | 2013-2015 | CNR, Florence, Italy |
| Eero Raisanen | grad student | 2015-2018 | CNR, Florence, Italy |
| Mario Mulansky | post-doc | 2014-2016 | CNR, Florence, Italy |
Publications
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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 |