Bibliographies thématiques / Spike train, data-analysis, SPIKE-distance

Littérature scientifique sur le sujet « Spike train, data-analysis, SPIKE-distance »

Auteur : Grafiati
Publié le 10 mars 2023

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Articles de revues sur le sujet "Spike train, data-analysis, SPIKE-distance"

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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, 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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Brown, Emery N., Riccardo Barbieri, Valérie Ventura, Robert E. Kass et Loren M. Frank. « The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis ». Neural Computation 14, no 2 (1 février 2002) : 325–46. http://dx.doi.org/10.1162/08997660252741149.

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Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments.We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.
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Koyama, Shinsuke, et Robert E. Kass. « Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons ». Neural Computation 20, no 7 (juillet 2008) : 1776–95. http://dx.doi.org/10.1162/neco.2008.06-07-540.

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Mathematical models of neurons are widely used to improve understanding of neuronal spiking behavior. These models can produce artificial spike trains that resemble actual spike train data in important ways, but they are not very easy to apply to the analysis of spike train data. Instead, statistical methods based on point process models of spike trains provide a wide range of data-analytical techniques. Two simplified point process models have been introduced in the literature: the time-rescaled renewal process (TRRP) and the multiplicative inhomogeneous Markov interval (m-IMI) model. In this letter we investigate the extent to which the TRRP and m-IMI models are able to fit spike trains produced by stimulus-driven leaky integrate-and-fire (LIF) neurons. With a constant stimulus, the LIF spike train is a renewal process, and the m-IMI and TRRP models will describe accurately the LIF spike train variability. With a time-varying stimulus, the probability of spiking under all three of these models depends on both the experimental clock time relative to the stimulus and the time since the previous spike, but it does so differently for the LIF, m-IMI, and TRRP models. We assessed the distance between the LIF model and each of the two empirical models in the presence of a time-varying stimulus. We found that while lack of fit of a Poisson model to LIF spike train data can be evident even in small samples, the m-IMI and TRRP models tend to fit well, and much larger samples are required before there is statistical evidence of lack of fit of the m-IMI or TRRP models. We also found that when the mean of the stimulus varies across time, the m-IMI model provides a better fit to the LIF data than the TRRP, and when the variance of the stimulus varies across time, the TRRP provides the better fit.
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Houghton, Conor, et Kamal Sen. « A New Multineuron Spike Train Metric ». Neural Computation 20, no 6 (juin 2008) : 1495–511. http://dx.doi.org/10.1162/neco.2007.10-06-350.

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The Victor-Purpura spike train metric has recently been extended to a family of multineuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The metric is one of the two metrics commonly used for quantifying the distance between two spike trains; the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multineuron metric. We believe this gives a metric that is both natural and easy to calculate. Both types of multineuron metric are applied to simulated data and are compared.
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Vargas-Irwin, Carlos E., David M. Brandman, Jonas B. Zimmermann, John P. Donoghue et Michael J. Black. « Spike Train SIMilarity Space (SSIMS) : A Framework for Single Neuron and Ensemble Data Analysis ». Neural Computation 27, no 1 (janvier 2015) : 1–31. http://dx.doi.org/10.1162/neco_a_00684.

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Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large-scale ensemble activity beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how spike train SIMilarity space (SSIMS) analysis captures the relationship between goal directions for an eight-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.
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Romero, Richard, et Tai Sing Lee. « Spike train analysis for single trial data ». Neurocomputing 44-46 (juin 2002) : 597–603. http://dx.doi.org/10.1016/s0925-2312(02)00446-0.

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Haslinger, Robert, Kristina Lisa Klinkner et Cosma Rohilla Shalizi. « The Computational Structure of Spike Trains ». Neural Computation 22, no 1 (janvier 2010) : 121–57. http://dx.doi.org/10.1162/neco.2009.12-07-678.

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Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically; (2) the randomness (internal entropy rate) of the minimal spike-generating process; and (3) a residual pure noise term not described by the minimal spike-generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred nonparametrically from the data, making only mild regularity assumptions, via the causal state splitting reconstruction algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability and spike train entropy, but also the complexity of a spike train's structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation.
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Brasselet, Romain, Roland S. Johansson et Angelo Arleo. « Quantifying Neurotransmission Reliability Through Metrics-Based Information Analysis ». Neural Computation 23, no 4 (avril 2011) : 852–81. http://dx.doi.org/10.1162/neco_a_00099.

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We set forth an information-theoretical measure to quantify neurotransmission reliability while taking into full account the metrical properties of the spike train space. This parametric information analysis relies on similarity measures induced by the metrical relations between neural responses as spikes flow in. Thus, in order to assess the entropy, the conditional entropy, and the overall information transfer, this method does not require any a priori decoding algorithm to partition the space into equivalence classes. It therefore allows the optimal parameters of a class of distances to be determined with respect to information transmission. To validate the proposed information-theoretical approach, we study precise temporal decoding of human somatosensory signals recorded using microneurography experiments. For this analysis, we employ a similarity measure based on the Victor-Purpura spike train metrics. We show that with appropriate parameters of this distance, the relative spike times of the mechanoreceptors’ responses convey enough information to perform optimal discrimination—defined as maximum metrical information and zero conditional entropy—of 81 distinct stimuli within 40 ms of the first afferent spike. The proposed information-theoretical measure proves to be a suitable generalization of Shannon mutual information in order to consider the metrics of temporal codes explicitly. It allows neurotransmission reliability to be assessed in the presence of large spike train spaces (e.g., neural population codes) with high temporal precision.
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Thèses sur le sujet "Spike train, data-analysis, SPIKE-distance"

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Echtermeyer, Christoph. « Causal pattern inference from neural spike train data ». Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Makhtar, Siti Noormiza Binti. « Conditional network measures using multivariate partial coherence analysis for spike train data with application to multi-electrode array recordings ». Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/18008/.

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This thesis proposes a novel approach for functional connectivity studies of neuronal signal recordings based on statistical signal processing analysis in the frequency domain using Multivariate Partial Coherence (MVPC) combined with network theory measures. MVPC is applied to spike trains signals to make inferences about the underlying network structure. The presence of connections between single unit spike trains is estimated using both coherence and MVPC analysis. Scalability of MVPC analysis is investigated through application to simulated spike train data with up to 100 simultaneous spike trains generated from a network of excitatory and inhibitory cortical neurons. Stable MVPC estimates were obtained with up to 198 predictors in partial coherence estimates, using a combination of simulated cortical neuron data and additional Poisson spike train predictors. MVPC provides higher order partial coherence analysis for multi-channel spike trains signals, removing effects of common influences in pairwise connectivity estimates. Network measures applied to binary and weighted adjacency measures derived from coherence and partial coherence are compared to determine the differences in unconditional and conditional networks of spike train interactions. A combination of MVPC analysis along with network theory analysis provides a systematic approach for multi-channel spike train signals. The proposed method is applied to simulated and multi-electrode array (MEA) spike train data. The MEA data consists of 19 single unit channels recorded from a study of connectivity in a model of kainic acid (KA) induced epileptiform activity for mesial temporal lobe epilepsy (mTLE) in a rat. The network theory analysis uses basic measures on both conditional and unconditional network, which highlights the differences in network structure and characteristics between the two representations. Complex analysis on conditional networks is useful in describing the properties of integration and segregation in the network.
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Chicharro, Raventós Daniel. « Characterization of information and causality measures for the study of neuronal data ». Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/22658.

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We study two methods of data analysis which are common tools for the analysis of neuronal data. In particular, we examine how causal interactions between brain regions can be investigated using time series reflecting the neural activity in these regions. Furthermore, we analyze a method used to study the neural code that evaluates the discrimination of the responses of single neurons elicited by different stimuli. This discrimination analysis is based on the quantification of the similarity of the spike trains with time scale parametric spike train distances. In each case we describe the methods used for the analysis of the neuronal data and we characterize their specificity using simulated or exemplary experimental data. Taking into account our results, we comment the previous studies in which the methods have been applied. In particular, we focus on the interpretation of the statistical measures in terms of underlying neuronal causal connectivity and properties of the neural code, respectively.
Estudiem dos mètodes d'anàlisi de dades que són eines habituals per a l'anàlisi de dades neuronals. Concretament, examinem la manera en què les interaccions causals entre regions del cervell poden ser investigades a partir de sèries temporals que reflecteixen l'activitat neuronal d'aquestes regions. A més a més, analitzem un mètode emprat per estudiar el codi neuronal que avalua la discriminació de les respostes de neurones individuals provocades per diferents estímuls. Aquesta anàlisi de la discriminació es basa en la quantificació de la similitud de les seqüències de potencials d'acció amb distàncies amb un paràmetre d'escala temporal. Tenint en compte els nostres resultats, comentem els estudis previs en els quals aquests mètodes han estat aplicats. Concretament, ens centrem en la interpretació de les mesures estadístiques en termes de connectivitat causal neuronal subjacent i propietats del codi neuronal, respectivament.
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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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Emhemmed, Yousef Mohammed. « Maximum likelihood analysis of neuronal spike trains ». Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326019.

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Somerville, Jared. « The exploration of neurophysiological spike train data using visual analytics ». Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/897.

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Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.
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Sanchez, Justin Cort. « From cortical neural spike trains to behavior modeling and analysis / ». [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0004289.

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Brody, Carlos Hopfield John J. Hopfield John J. « Analysis and modeling of spike train correlations in the lateral geniculate nucleus / ». Diss., Pasadena, Calif. : California Institute of Technology, 1998. http://resolver.caltech.edu/CaltechETD:etd-01182008-092108.

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Malvestio, Irene. « Detection of directional interactions between neurons from spike trains ». Doctoral thesis, Universitat Pompeu Fabra, 2019. http://hdl.handle.net/10803/666226.

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An important problem in neuroscience is the assessment of the connectivity between neurons from their spike trains. One recent approach developed for the detection of directional couplings between dynamics based on recorded point processes is the nonlinear interdependence measure L. In this thesis we first use the Hindmarsh-Rose model system to test L in the presence of noise and for different spiking regimes of the dynamics. We then compare the performance of L against the linear cross-correlogram and two spike train distances. Finally, we apply all measures to neuronal spiking data from an intracranial whole-night recording of a patient with epilepsy. When applied to simulated data, L proves to be versatile, robust and more sensitive than the linear measures. Instead, in the real data the linear measures find more connections than L, in particular for neurons in the same brain region and during slow wave sleep.
Un problema important en la neurociència és determinar la connexió entre neurones utilitzant dades dels seus trens d’impulsos. Un mètode recent que afronta la detecció de connexions direccionals entre dinàmiques utilitzant processos puntuals és la mesura d’interdependència no lineal L. En aquesta tesi, utilitzem el model de Hindmarsh-Rose per testejar L en presència de soroll i per diferents règims dinàmics. Després comparem el desempenyorament de L en comparació al correlograma lineal i a dues mesures de trens d’impulsos. Finalment, apliquem totes aquestes mesures a dades d’impulsos de neurones obtingudes de senyals intracranials electroencefalogràfiques gravades durant una nit a un pacient amb epilèpsia. Quan utilitzem dades simulades, L demostra que és versàtil, robusta i més sensible que les mesures lineals. En canvi, utilitzant dades reals, les mesures lineals troben més connexions que L, especialment entre neurones en la mateixa àrea del cervell i durant la fase de son d’ones lentes.
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Brooks, Evan Monticino Michael G. « Determining properties of synaptic structure in a neural network through spike train analysis ». [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3702.

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Livres sur le sujet "Spike train, data-analysis, SPIKE-distance"

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Grün, Sonja, et Stefan Rotter, dir. Analysis of Parallel Spike Trains. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5675-0.

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Rotter, Stefan, et Sonja Grün. Analysis of Parallel Spike Trains. Springer London, Limited, 2010.

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Analysis Of Parallel Spike Trains. Springer, 2010.

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Rotter, Stefan, et Sonja Grün. Analysis of Parallel Spike Trains. Springer, 2012.

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Chapitres de livres sur le sujet "Spike train, data-analysis, SPIKE-distance"

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Braune, Christian, Christian Borgelt et Sonja Grün. « Assembly Detection in Continuous Neural Spike Train Data ». Dans Advances in Intelligent Data Analysis XI, 78–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34156-4_9.

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Brillinger, David R. « Nerve Cell Spike Train Data Analysis : A Progression of Technique ». Dans Selected Works of David Brillinger, 577–88. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1344-8_33.

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Halliday, David M., et Jay R. Rosenberg. « Time and Frequency Domain Analysis of Spike Train and Time Series Data ». Dans Modern Techniques in Neuroscience Research, 503–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58552-4_18.

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Chen, Zhe, et Emery N. Brown. « State-Space Models for the Analysis of Neural Spike Train and Behavioral Data ». Dans Encyclopedia of Computational Neuroscience, 2864–67. New York, NY : Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_410.

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Chen, Zhe, et Emery N. Brown. « State-Space Models for the Analysis of Neural Spike Train and Behavioral Data ». Dans Encyclopedia of Computational Neuroscience, 1–4. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_410-1.

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Victor, Jonathan D., et Keith P. Purpura. « Spike Metrics ». Dans Analysis of Parallel Spike Trains, 129–56. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5675-0_7.

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Grün, Sonja. « Spike Train Analysis : Overview ». Dans Encyclopedia of Computational Neuroscience, 92–94. New York, NY : Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_776.

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Grün, Sonja. « Spike Train Analysis : Overview ». Dans Encyclopedia of Computational Neuroscience, 1–2. New York, NY : Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-7320-6_776-1.

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Grün, Sonja. « Spike Train Analysis : Overview ». Dans Encyclopedia of Computational Neuroscience, 1–3. New York, NY : Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-7320-6_776-2.

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van Vreeswijk, Carl. « Stochastic Models of Spike Trains ». Dans Analysis of Parallel Spike Trains, 3–20. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5675-0_1.

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Actes de conférences sur le sujet "Spike train, data-analysis, SPIKE-distance"

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Shekhar, Shubhanshu, et Kaushik Majumdar. « A new spike train distance measure ». Dans 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012. http://dx.doi.org/10.1109/icdse.2012.6281906.

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Gainutdinov, Azat. « Method for analyzing the inhibition of cellular signals in the spike train format ». Dans Computations and Data Analysis : from Molecular Processes to Brain Functions, sous la direction de Dmitry E. Postnov. SPIE, 2021. http://dx.doi.org/10.1117/12.2591330.

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Patnaik, Debprakash, Srivatsan Laxman et Naren Ramakrishnan. « Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis ». Dans 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.73.

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« Session TA2a : Neural spike train analysis ». Dans 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094551.

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Paiva, Antonio, Il Park et Jose C. Principe. « Innovating Signal Processing for Spike Train Data ». Dans 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4353572.

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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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Zheng, Xuyuan, Mingui Sun et Xin Tian. « Wavelet Entropy Analysis of Neural Spike Train ». Dans 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.530.

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Amin, H. H., et R. H. Fujii. « Spike train learning algorithm, applications, and analysis ». Dans 48th Midwest Symposium on Circuits and Systems, 2005. IEEE, 2005. http://dx.doi.org/10.1109/mwscas.2005.1594193.

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Paiva, Antonio R. C., Il Park et Jose C. Principe. « Reproducing kernel Hilbert spaces for spike train analysis ». Dans ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518834.

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Nomura, Masaki, Yoshio Sakurai et Toshio Aoyagi. « Kernel Analysis Of Multi-neuronal Spike Trains ». Dans 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/iccme.2007.4381694.

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