Bibliographies thématiques / Spike Train Synchrony

Littérature scientifique sur le sujet « Spike Train Synchrony »

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Publié le 10 mars 2023

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Articles de revues sur le sujet "Spike Train Synchrony"

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Ciba, Manuel, Robert Bestel, Christoph Nick, Guilherme Ferraz de Arruda, Thomas Peron, Comin César Henrique, Luciano da Fontoura Costa, Francisco Aparecido Rodrigues et Christiane Thielemann. « Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity ». Neural Computation 32, no 5 (mai 2020) : 887–911. http://dx.doi.org/10.1162/neco_a_01277.

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As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains.
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Kreuz, Thomas, Daniel Chicharro, Conor Houghton, Ralph G. Andrzejak et Florian Mormann. « Monitoring spike train synchrony ». Journal of Neurophysiology 109, no 5 (1 mars 2013) : 1457–72. http://dx.doi.org/10.1152/jn.00873.2012.

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Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.
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Kreuz, Thomas, Julie S. Haas, Alice Morelli, Henry D. I. Abarbanel et Antonio Politi. « Measuring spike train synchrony ». Journal of Neuroscience Methods 165, no 1 (septembre 2007) : 151–61. http://dx.doi.org/10.1016/j.jneumeth.2007.05.031.

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Kreuz, Thomas, Daniel Chicharro, Ralph G. Andrzejak, Julie S. Haas et Henry D. I. Abarbanel. « Measuring multiple spike train synchrony ». Journal of Neuroscience Methods 183, no 2 (octobre 2009) : 287–99. http://dx.doi.org/10.1016/j.jneumeth.2009.06.039.

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Kreuz, Thomas. « Measures of spike train synchrony ». Scholarpedia 6, no 10 (2011) : 11934. http://dx.doi.org/10.4249/scholarpedia.11934.

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Brody, Carlos D. « Correlations Without Synchrony ». Neural Computation 11, no 7 (1 octobre 1999) : 1537–51. http://dx.doi.org/10.1162/089976699300016133.

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Peaks in spike train correlograms are usually taken as indicative of spike timing synchronization between neurons. Strictly speaking, however, a peak merely indicates that the two spike trains were not independent. Two biologically plausible ways of departing from independence that are capable of generating peaks very similar to spike timing peaks are described here: covariations over trials in response latency and covariations over trials in neuronal excitability. Since peaks due to these interactions can be similar to spike timing peaks, interpreting a correlogram may be a problem with ambiguous solutions. What peak shapes do latency or excitability interactions generate? When are they similar to spike timing peaks? When can they be ruled out from having caused an observed correlogram peak? These are the questions addressed here. The previous article in this issue proposes quantitative methods to tell cases apart when latency or excitability covariations cannot be ruled out.
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Kreuz, Thomas, Mario Mulansky et Nebojsa Bozanic. « SPIKY : a graphical user interface for monitoring spike train synchrony ». Journal of Neurophysiology 113, no 9 (mai 2015) : 3432–45. http://dx.doi.org/10.1152/jn.00848.2014.

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Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.
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Houghton, Conor. « Population measures of spike train synchrony ». Scholarpedia 8, no 10 (2013) : 30635. http://dx.doi.org/10.4249/scholarpedia.30635.

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Kajikawa, Yoshinao, et Troy A. Hackett. « Entropy analysis of neuronal spike train synchrony ». Journal of Neuroscience Methods 149, no 1 (novembre 2005) : 90–93. http://dx.doi.org/10.1016/j.jneumeth.2005.05.011.

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Chen, Zhe. « An Overview of Bayesian Methods for Neural Spike Train Analysis ». Computational Intelligence and Neuroscience 2013 (2013) : 1–17. http://dx.doi.org/10.1155/2013/251905.

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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.
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Thèses sur le sujet "Spike Train Synchrony"

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Pazienti, Antonio. « Manipulations of spike trains and their impact on synchrony analysis ». Phd thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2008/1744/.

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The interaction between neuronal cells can be identified as the computing mechanism of the brain. Neurons are complex cells that do not operate in isolation, but they are organized in a highly connected network structure. There is experimental evidence that groups of neurons dynamically synchronize their activity and process brain functions at all levels of complexity. A fundamental step to prove this hypothesis is to analyze large sets of single neurons recorded in parallel. Techniques to obtain these data are meanwhile available, but advancements are needed in the pre-processing of the large volumes of acquired data and in data analysis techniques. Major issues include extracting the signal of single neurons from the noisy recordings (referred to as spike sorting) and assessing the significance of the synchrony. This dissertation addresses these issues with two complementary strategies, both founded on the manipulation of point processes under rigorous analytical control. On the one hand I modeled the effect of spike sorting errors on correlated spike trains by corrupting them with realistic failures, and studied the corresponding impact on correlation analysis. The results show that correlations between multiple parallel spike trains are severely affected by spike sorting, especially by erroneously missing spikes. When this happens sorting strategies characterized by classifying only good'' spikes (conservative strategies) lead to less accurate results than tolerant'' strategies. On the other hand, I investigated the effectiveness of methods for assessing significance that create surrogate data by displacing spikes around their original position (referred to as dithering). I provide analytical expressions of the probability of coincidence detection after dithering. The effectiveness of spike dithering in creating surrogate data strongly depends on the dithering method and on the method of counting coincidences. Closed-form expressions and bounds are derived for the case where the dither equals the allowed coincidence interval. This work provides new insights into the methodologies of identifying synchrony in large-scale neuronal recordings, and of assessing its significance.
Die Informationsverarbeitung im Gehirn erfolgt maßgeblich durch interaktive Prozesse von Nervenzellen, sogenannten Neuronen. Diese zeigen eine komplexe Dynamik ihrer chemischen und elektrischen Eigenschaften. Es gibt deutliche Hinweise darauf, dass Gruppen synchronisierter Neurone letztlich die Funktionsweise des Gehirns auf allen Ebenen erklären können. Um die schwierige Frage nach der genauen Funktionsweise des Gehirns zu beantworten, ist es daher notwendig, die Aktivität vieler Neuronen gleichzeitig zu messen. Die technischen Voraussetzungen hierfür sind in den letzten Jahrzehnten durch Multielektrodensyteme geschaffen worden, die heute eine breite Anwendung finden. Sie ermöglichen die simultane extrazelluläre Ableitung von bis zu mehreren hunderten Kanälen. Die Voraussetzung für die Korrelationsanalyse von vielen parallelen Messungen ist zunächst die korrekte Erkennung und Zuordnung der Aktionspotentiale einzelner Neurone, ein Verfahren, das als Spikesortierung bezeichnet wird. Eine weitere Herausforderung ist die statistisch korrekte Bewertung von empirisch beobachteten Korrelationen. Mit dieser Dissertationsschrift lege ich eine theoretische Arbeit vor, die sich der Vorverarbeitung der Daten durch Spikesortierung und ihrem Einfluss auf die Genauigkeit der statistischen Auswertungsverfahren, sowie der Effektivität zur Erstellung von Surrogatdaten für die statistische Signifikanzabschätzung auf Korrelationen widmet. Ich verwende zwei komplementäre Strategien, die beide auf der analytischen Berechnung von Punktprozessmanipulationen basieren. In einer ausführlichen Studie habe ich den Effekt von Spikesortierung in mit realistischen Fehlern behafteten korrelierten Spikefolgen modeliert. Zum Vergleich der Ergebnisse zweier unterschiedlicher Methoden zur Korrelationsanalyse auf den gestörten, sowie auf den ungestörten Prozessen, leite ich die entsprechenden analytischen Formeln her. Meine Ergebnisse zeigen, dass koinzidente Aktivitätsmuster multipler Spikefolgen durch Spikeklassifikation erheblich beeinflusst werden. Das ist der Fall, wenn Neuronen nur fälschlicherweise Spikes zugeordnet werden, obwohl diese anderen Neuronen zugehörig sind oder Rauschartefakte sind (falsch positive Fehler). Jedoch haben falsch-negative Fehler (fälschlicherweise nicht-klassifizierte oder missklassifizierte Spikes) einen weitaus grösseren Einfluss auf die Signifikanz der Korrelationen. In einer weiteren Studie untersuche ich die Effektivität einer Klasse von Surrogatmethoden, sogenannte Ditheringverfahren, welche paarweise Korrelationen zerstören, in dem sie koinzidente Spikes von ihrer ursprünglichen Position in einem kleinen Zeitfenster verrücken. Es zeigt sich, dass die Effektivität von Spike-Dithering zur Erzeugung von Surrogatdaten sowohl von der Dithermethode als auch von der Methode zur Koinzidenzzählung abhängt. Für die Wahrscheinlichkeit der Koinzidenzerkennung nach dem Dithern stelle ich analytische Formeln zur Verfügung. Die vorliegende Arbeit bietet neue Einblicke in die Methoden zur Korrelationsanalyse auf multi-variaten Punktprozessen mit einer genauen Untersuchung von unterschiedlichen statistischen Einflüssen auf die Signifikanzabschätzung. Für die praktische Anwendung ergeben sich Leitlinien für den Umgang mit Daten zur Synchronizitätsanalyse.
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Torre, Emiliano [Verfasser], Sonja [Verfasser] Grün, Björn [Akademischer Betreuer] Kampa et Laura [Akademischer Betreuer] Sacerdote. « Statistical analysis of synchrony and synchrony propagation in massively parallel spike trains / Emiliano Torre, Sonja Grün ; Björn Kampa, Laura Sacerdote ». Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/1125911522/34.

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Zhou, Pengcheng. « Computational Tools for Identification and Analysis of Neuronal Population Activity ». Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/1015.

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Recently-developed technologies for monitoring activity in populations of neurons make it possible for the first time, in principle, to ask many basic questions in neuroscience. However, computational tools for analyzing newly available data need to be developed. The goal of this thesis is to contribute to this effort by focusing on two specific problems. First, we used a point-process regression framework to provide a methodology for statistical assessment of the link between neural spike synchrony and network-wide oscillations. In simulations, we showed that our method can recover ground-truth relationships, and in two types of spike train data we illustrated the kinds of results the method can produce. The approach improves on methods in the literature and may be adapted to many different experimental settings. Second, we considered the problem of source extraction in calcium imaging data, i.e., the detection of neurons within a field of view and the extraction of each neuron’s activity. The data we mainly focus on are recorded with a microendoscope, which has the unique advantage of imaging deep brain regions in freely behaving animals. These data suffer from high levels of background fluorescence, as well as the potential for overlapping neuronal signals. Based on the existing constrained nonnegative matrix factorization (CNMF) framework, we developed an efficient method to process microendoscopic data. Our method utilizes a novel algorithm to initialize the spatial shapes and temporal activity of the neurons from the raw video data independently from the strong fluctuating background. This step ensures the efficiency and accuracy of solving a nonconvex CNMF problem. Our method also models the complicated background by including its low-spatial frequency structure and the locally-low-rank feature to avoid absorbing cellular signals into the background term. We developed a tractable solution to estimate the background activity using this new model. After subtracting the approximated background, we followed the CNMF framework to demix neural signals and recover denoised and deconvolved temporal activity. We optimized several algorithms in solving the CNMF problems to get accurate results. In practice, our method outperforms all existing methods and has been adopted by many experimental labs.
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Ciba, Manuel [Verfasser], Andreas [Gutachter] Bahmer, Charlotte [Gutachter] Förster et Christiane [Gutachter] Thielemann. « Synchrony Measurement and Connectivity Estimation of Parallel Spike Trains from in vitro Neuronal Networks / Manuel Ciba ; Gutachter : Andreas Bahmer, Charlotte Förster, Christiane Thielemann ». Würzburg : Universität Würzburg, 2021. http://d-nb.info/1229352341/34.

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Herrera-Valdez, Marco Arieli. « Relationship Between Nearly-Coincident Spiking and Common Excitatory Synaptic Input in Motor Neurons ». Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/196051.

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The activities of pairs of mammalian motor neurons (MNs) receiving varying degrees of common excitatory synaptic input were simulated to study the relationship between nearly-coincident spiking and common excitatory drive. The somatic membrane potential of each MN was modeled using a single compartment model. Each MN was modeled toreceive synaptic contacts from hundreds of pre-synaptic fibers. The percentage of pre-synaptic fibers that diverged to supply both MNs of a pair with common synaptic input could be varied from 0 (no common inputs) to 100% (all common inputs). Spikes trains on separate re-synaptic fibers were independent of one another and were modeled as realizations of renewal processes with mean firing rates (10 - 50Hz) resembling that associated with supra-spinal input. Maximum synaptic conductances and time constants were varied across synapsesto match experimentally observed somatic EPSPs. The number of active pre-synaptic fibers to each MN was adjusted in order that the firingrates of MNs were between 8 and 15 Hz. For each common input condition, 100 s of concurrent spiking activity of the MNs was usedto construct cross-correlation histograms. The sizes of the central peaks in the histograms were quantified using both the k' (Ellaway and Murthy 1985) and CIS (Nordstrom et al. 1992) indices ofsynchrony. Monotonically increasing linear relationships between the proportion of common synaptic input and the magnitude of synchronywere observed for both indices. For example, the model predicted that 10% common input would yield a CIS value of 0.27 whereas 100% commoninput would yield a CIS value of 1.5. These values are within the range of values observed experimentally. These results, therefore,provide a means to translate measures of nearly-coincident spiking into plausible renditions of synaptic connectivity.
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BOZANIC, NEBOJSA. « Measures of spike train synchrony ». Doctoral thesis, 2016. http://hdl.handle.net/2158/1043650.

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In experimental neuroscience techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years time-resolved measures of spike train synchrony such as the ISI-distance and the SPIKE-distance have been proposed. Here we add the complementary measure SPIKE-synchronization, a sophisticated multivariate coincidence detector with a very intuitive interpretation. In the first Results chapter we present SPIKY, an interactive graphical user interface that facilitates the application of these three time-resolved measures of spike train synchrony to both simulated and real data. SPIKY, which has been optimized with respect to computation speed and memory demand, also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. In the second Results chapter we deal with the very important problem of latency variations in real data. By means of a validated setup we can show that the parameter-free SPIKE-distance outperforms two time-scale dependent standard measures. In summary, in this thesis we provide several important measures and corrections that when applied to the right experimental datasets could potentially lead to an increased understanding of the neural code - the ultimate goal of neuroscience.
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Pazienti, Antonio [Verfasser]. « Manipulations of spike trains and their impact on synchrony analysis / von Antonio Pazienti ». 2007. http://d-nb.info/988948524/34.

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Ciba, Manuel. « Synchrony Measurement and Connectivity Estimation of Parallel Spike Trains from in vitro Neuronal Networks ». Doctoral thesis, 2021. https://doi.org/10.25972/OPUS-22364.

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The goal of this doctoral thesis is to identify appropriate methods for the estimation of connectivity and for measuring synchrony between spike trains from in vitro neuronal networks. Special focus is set on the parameter optimization, the suitability for massively parallel spike trains, and the consideration of the characteristics of real recordings. Two new methods were developed in the course of the optimization which outperformed other methods from the literature. The first method “Total spiking probability edges” (TSPE) estimates the effective connectivity of two spike trains, based on the cross-correlation and a subsequent analysis of the cross-correlogram. In addition to the estimation of the synaptic weight, a distinction between excitatory and inhibitory connections is possible. Compared to other methods, simulated neuronal networks could be estimated with higher accuracy, while being suitable for the analysis of massively parallel spike trains. The second method “Spike-contrast” measures the synchrony of parallel spike trains with the advantage of automatically optimizing its time scale to the data. In contrast to other methods, which also adapt to the characteristics of the data, Spike-contrast is more robust to erroneous spike trains and significantly faster for large amounts of parallel spike trains. Moreover, a synchrony curve as a function of the time scale is generated by Spike-contrast. This optimization curve is a novel feature for the analysis of parallel spike trains
Ziel dieser Dissertation ist die Identifizierung geeigneter Methoden zur Schätzung der Konnektivität und zur Messung der Synchronität von in-vitro Spike-Trains. Besonderes Augenmerk wird dabei auf die Parameteroptimierung, die Eignung für große Mengen paralleler Spike-Trains und die Berücksichtigung der Charakteristik von realen Aufnahmen gelegt. Im Zuge der Optimierung wurden zwei neue Methoden entwickelt, die anderen Methoden aus der Literatur überlegen waren. Die erste Methode “Total spiking probability edges” (TSPE) schätzt die effektive Konnektivität zwischen zwei Spike-Trains basierend auf der Berechnung einer Kreuzkorrelation und einer anschließenden Analyse des Kreuzkorrelograms. Neben der Schätzung der synaptischen Ge- wichtung ist eine Unterscheidung zwischen exzitatorischen und inhibitorischen Verbindungen möglich. Im Vergleich zu anderen Methoden, konnten simulierte neuronale Netzwerke mit einer höheren Genauigkeit geschätzt werden. Zudem ist TSPE aufgrund der hohen Rechengeschwindigkeit für große Datenmengen geeignet. Die zweite Methode “Spike-contrast” misst die Synchronität paralleler Spike-Trains mit dem Vorteil, dass die Zeitskala automatisch an die Daten angepasst wird. Im Gegensatz zu anderen Methoden, welche sich ebenfalls an die Daten anpassen, ist Spike-contrast robuster gegenüber fehlerhaften Spike-Trains und schneller für große Datenmengen. Darüber hinaus berechnet Spike-Contrast eine Synchronitätskurve als Funktion der Zeitskala. Diese Kurve ist ein neuartiges Feature zur Analyse paralleler Spike-Trains
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Chapitres de livres sur le sujet "Spike Train Synchrony"

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Satuvuori, Eero, Irene Malvestio et Thomas Kreuz. « Measures of Spike Train Synchrony and Directionality ». Dans Mathematical and Theoretical Neuroscience, 201–22. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68297-6_13.

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Qu, Jingyi, Rubin Wang, Ying Du et Chuankui Yan. « An Improved Method of Measuring Multiple Spike Train Synchrony ». Dans Advances in Cognitive Neurodynamics (V), 777–83. Singapore : Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0207-6_105.

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Houghton, Conor, et Thomas Kreuz. « Measures of Spike Train Synchrony : From Single Neurons to Populations ». Dans Multiscale Analysis and Nonlinear Dynamics, 277–97. Weinheim, Germany : Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527671632.ch13.

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Actes de conférences sur le sujet "Spike Train Synchrony"

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Lama, Nikesh, Alan Hargreaves, Bob Stevens et TM McGinnity. « Spike Train Synchrony Analysis of Neuronal Cultures ». Dans 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489728.

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Mulansky, Mario, Nebojsa Bozanic, Andreea Sburlea et Thomas Kreuz. « A guide to time-resolved and parameter-free measures of spike train synchrony ». Dans 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP). IEEE, 2015. http://dx.doi.org/10.1109/ebccsp.2015.7300693.

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Chew, Gabriel, Kai Keng Ang, Rosa Q. So, Zhiming Xu et Cuntai Guan. « Combining firing rate and spike-train synchrony features in the decoding of motor cortical activity ». Dans 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318555.

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Qi, Dexuan, et Zhenguo Xiao. « Spike trains synchrony with different coupling strengths in a hippocampus CA3 small-world network model ». Dans 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2013. http://dx.doi.org/10.1109/bmei.2013.6746947.

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Qi, Dexuan, Zhenguo Xiao, Shuo Liu et Yongshu Jiao. « Spike Trains Synchrony with Changed Neuronal Networks Parameters in a Hippocampus CA3 Small-World Network Model ». Dans 2017 4th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2017. http://dx.doi.org/10.1109/icisce.2017.360.

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