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  1. Book ; Online ; Thesis: Measuring synchronization in model systems and electroencephalographic time series from epilepsy patients

    Kreuz, Thomas

    2004  

    Author's details by Thomas Kreuz
    Language English
    Size 1 Computerdatei (ca. 10 MB)
    Publisher Dep. of Physics, Univ. of Wuppertal
    Publishing place Wuppertal
    Publishing country Germany
    Document type Book ; Online ; Thesis
    Thesis / German Habilitation thesis Wuppertal, Univ., Diss., 2003
    Note Open Access
    Accompanying material Auszüge (Title, Summary, Zusammenfassung, Contents, ca. 22 KB)
    HBZ-ID HT013975090
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book: Enthaftung des Geschäftsleiters einer Kapitalgesellschaft durch Rechtsrat

    Kreuz, Thomas

    (Schriften zum Handels- und Gesellschaftsrecht ; 168)

    2015  

    Author's details Thomas Kreuz
    Series title Schriften zum Handels- und Gesellschaftsrecht ; 168
    Keywords Geschäftsführer ; Pflichtverletzung ; Rechtspflicht ; Kapitalgesellschaft ; Rechtsberatung ; Deutschland
    Language German
    Size ILII, 101 S., 210 mm x 148 mm, 194 g
    Edition 1. Aufl.
    Publisher Kovac̆
    Publishing place Hamburg
    Document type Book
    ISBN 3830082762 ; 9783830082767
    Database Former special subject collection: coastal and deep sea fishing

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  3. Article ; Online: Which spike train distance is most suitable for distinguishing rate and temporal coding?

    Satuvuori, Eero / Kreuz, Thomas

    Journal of neuroscience methods

    2018  Volume 299, Page(s) 22–33

    Abstract: Background: It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as ...

    Abstract Background: It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance.
    New method: We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates?
    Results: Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only.
    Comparison with existing methods: While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates.
    Conclusions: The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data.
    MeSH term(s) Action Potentials ; Animals ; Data Interpretation, Statistical ; Humans ; Models, Neurological ; Neurons ; Poisson Distribution ; Time Factors
    Language English
    Publishing date 2018-02-17
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 282721-9
    ISSN 1872-678X ; 0165-0270
    ISSN (online) 1872-678X
    ISSN 0165-0270
    DOI 10.1016/j.jneumeth.2018.02.009
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  4. Article ; Online: PySpike-A Python library for analyzing spike train synchrony

    Mulansky, Mario / Kreuz, Thomas

    SoftwareX. 2016,

    2016  

    Abstract: Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a ... ...

    Abstract Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.
    Keywords Synchrony ; Spike train analysis ; Spike train distance ; Python
    Language English
    Publishing place Elsevier B.V.
    Document type Article ; Online
    Note Pre-press version
    ZDB-ID 2819369-6
    ISSN 2352-7110
    ISSN 2352-7110
    DOI 10.1016/j.softx.2016.07.006
    Database NAL-Catalogue (AGRICOLA)

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  5. Book ; Online: Measures of spike train synchrony and directionality

    Satuvuori, Eero / Malvestio, Irene / Kreuz, Thomas

    2020  

    Abstract: Measures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and SPIKE-synchronization have already been successfully applied in ...

    Abstract Measures of spike train synchrony have become important tools in both experimental and theoretical neuroscience. Three time-resolved measures called the ISI-distance, the SPIKE-distance, and SPIKE-synchronization have already been successfully applied in many different contexts. These measures are time scale independent, since they consider all time scales as equally important. However, in real data one is typically less interested in the smallest time scales and a more adaptive approach is needed. Therefore, in the first part of this Chapter we describe recently introduced generalizations of the three measures, that gradually disregard differences in smaller time-scales. Besides similarity, another very relevant property of spike trains is the temporal order of spikes. In the second part of this chapter we address this property and describe a very recently proposed algorithm, which quantifies the directionality within a set of spike train. This multivariate approach sorts multiple spike trains from leader to follower and quantifies the consistency of the propagation patterns. Finally, all measures described in this chapter are freely available for download.

    Comment: 20 pages, 6 figures, book chapter. arXiv admin note: substantial text overlap with arXiv:1610.07986, arXiv:1702.05394
    Keywords Physics - Data Analysis ; Statistics and Probability
    Subject code 612
    Publishing date 2020-01-13
    Publishing country us
    Document type Book ; Online
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  6. Article ; Online: Using spike train distances to identify the most discriminative neuronal subpopulation.

    Satuvuori, Eero / Mulansky, Mario / Daffertshofer, Andreas / Kreuz, Thomas

    Journal of neuroscience methods

    2018  Volume 308, Page(s) 354–365

    Abstract: Background: Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have ... ...

    Abstract Background: Spike trains of multiple neurons can be analyzed following the summed population (SP) or the labeled line (LL) hypothesis. Responses to external stimuli are generated by a neuronal population as a whole or the individual neurons have encoding capacities of their own. The SPIKE-distance estimated either for a single, pooled spike train over a population or for each neuron separately can serve to quantify these responses.
    New method: For the SP case we compare three algorithms that search for the most discriminative subpopulation over all stimulus pairs. For the LL case we introduce a new algorithm that combines neurons that individually separate different pairs of stimuli best.
    Results: The best approach for SP is a brute force search over all possible subpopulations. However, it is only feasible for small populations. For more realistic settings, simulated annealing clearly outperforms gradient algorithms with only a limited increase in computational load. Our novel LL approach can handle very involved coding scenarios despite its computational ease.
    Comparison with existing methods: Spike train distances have been extended to the analysis of neural populations interpolating between SP and LL coding. This includes parametrizing the importance of distinguishing spikes being fired in different neurons. Yet, these approaches only consider the population as a whole. The explicit focus on subpopulations render our algorithms complimentary.
    Conclusions: The spectrum of encoding possibilities in neural populations is broad. The SP and LL cases are two extremes for which our algorithms provide correct identification results.
    MeSH term(s) Action Potentials/physiology ; Algorithms ; Animals ; Computer Simulation ; Data Interpretation, Statistical ; Humans ; Models, Neurological ; Neurons ; Pattern Recognition, Automated/methods
    Language English
    Publishing date 2018-09-10
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 282721-9
    ISSN 1872-678X ; 0165-0270
    ISSN (online) 1872-678X
    ISSN 0165-0270
    DOI 10.1016/j.jneumeth.2018.09.008
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  7. Article ; Online: Latency correction in sparse neuronal spike trains.

    Kreuz, Thomas / Senocrate, Federico / Cecchini, Gloria / Checcucci, Curzio / Mascaro, Anna Letizia Allegra / Conti, Emilia / Scaglione, Alessandro / Pavone, Francesco Saverio

    Journal of neuroscience methods

    2022  Volume 381, Page(s) 109703

    Abstract: Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious ... ...

    Abstract Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected.
    New method: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing.
    Results: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset.
    Comparison with existing method(s): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information.
    Conclusions: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.
    MeSH term(s) Action Potentials/physiology ; Algorithms ; Animals ; Calcium ; Mice ; Models, Neurological ; Neurons/physiology ; Noise
    Chemical Substances Calcium (SY7Q814VUP)
    Language English
    Publishing date 2022-09-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 282721-9
    ISSN 1872-678X ; 0165-0270
    ISSN (online) 1872-678X
    ISSN 0165-0270
    DOI 10.1016/j.jneumeth.2022.109703
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  8. Article ; Online: Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains.

    Malvestio, Irene / Kreuz, Thomas / Andrzejak, Ralph G

    Physical review. E

    2017  Volume 96, Issue 2-1, Page(s) 22203

    Abstract: The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of ... ...

    Abstract The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.
    Language English
    Publishing date 2017-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.96.022203
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  9. Article ; Online: On the efficient calculation of van Rossum distances.

    Houghton, Conor / Kreuz, Thomas

    Network (Bristol, England)

    2012  Volume 23, Issue 1-2, Page(s) 48–58

    Abstract: The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all ... ...

    Abstract The van Rossum metric measures the distance between two spike trains. Measuring a single van Rossum distance between one pair of spike trains is not a computationally expensive task, however, many applications require a matrix of distances between all the spike trains in a set or the calculation of a multi-neuron distance between two populations of spike trains. Moreover, often these calculations need to be repeated for many different parameter values. An algorithm is presented here to render these calculation less computationally expensive, making the complexity linear in the number of spikes rather than quadratic.
    MeSH term(s) Algorithms ; Computers ; Electrophysiology/methods ; Linear Models ; Models, Neurological ; Neurons/physiology ; Poisson Distribution ; Software
    Language English
    Publishing date 2012
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1026760-8
    ISSN 1361-6536 ; 0954-898X
    ISSN (online) 1361-6536
    ISSN 0954-898X
    DOI 10.3109/0954898X.2012.673048
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  10. Book ; Online: Latency correction in sparse neuronal spike trains

    Kreuz, Thomas / Senocrate, Federico / Cecchini, Gloria / Checcucci, Curzio / Mascaro, Anna Letizia Allegra / Conti, Emilia / Scaglione, Alessandro / Pavone, Francesco Saverio

    2022  

    Abstract: Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious ... ...

    Abstract Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. New Method: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. Results: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. Comparison with Existing Method(s): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. Conclusions: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

    Comment: 15 pages, 10 figures
    Keywords Physics - Data Analysis ; Statistics and Probability ; Mathematics - Dynamical Systems ; Physics - Biological Physics ; Statistics - Methodology
    Subject code 612
    Publishing date 2022-05-19
    Publishing country us
    Document type Book ; Online
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