Modern high-performance digital signal processing (DSP) applications face constantly increasing performance requirements and are becoming increasingly challenging to develop and work with. In DSP paradigm, many researchers see potential in achieving algorithm speed-up by employing Field Programmable Gate Arrays (FPGAs) – reconfigurable hardware with parallelism feature. However, developing applications for FPGAs incur particular challenges on the development flow. This work proposes a scalable hybrid DSP system for performing high-performance signal processing applications. The system employs hybrid CPU + FPGA architecture of commercially available, off-the-shelf (COTS) FPGAs and central processing units (CPU) of personal computers. In this work an example implementation of a multi-channel cross-correlator is investigated and delivered using a new development paradigm. The correlator is implemented on the XD1000 development system using a high-level FPGA programming tool – Impulse CoDeveloper. Analysis of DSP application development in a hybrid CPU+FPGA system employing the high-level programming tool Impulse C is presented. Potential of the selected tool to deliver algorithm speed-ups is investigated using reference multi-channel correlator software. Particular attention is devoted to input/output (I/O) implementation, which is considered one of the most challenging problems in FPGA design development. This work delivers an I/O framework based on PCI Express interface for the proposed high-performance scalable DSP system. Using Stratix II GX PCI Express Development Board from Altera Corporation, a scalable and flexible communication approach for the multi-channel correlator is delivered. This framework can be adapted to perform other high-performance streaming DSP applications. The outcomes of this work are a multi-channel correlator developed in a reconfigurable environment with new design methodology and I/O framework with software control application. The outcomes are used to demonstrate the potential of implementing DSP applications in hybrid CPU + FPGA architecture and to discuss existing challenges and suggest possible solutions.

Multiple-Input Multiple-Output (MIMO) communications systems using multiantenna arrays simultaneously during transmission and reception have generated significant interest in recent years. Theoretical work in the mid 1990?s showed the potential for significant capacity increases in wireless channels via spatial multiplexing with sparse antenna arrays and rich scattering environments. However, in reality the capacity is significantly reduced when the antennas are placed close together, or the scattering environment is sparse, causing the signals received by different antennas to become correlated, corresponding to a reduction of the effective number of sub-channels between transmit and receive antennas. By introducing the previously ignored spatial aspects, namely the antenna array geometry and the scattering environment, into a novel channel model new bounds and fundamental limitations to MIMO capacity are derived for spatially constrained, or spatially selective, channels. A theoretically derived capacity saturation point is shown to exist for spatially selective MIMO channels, at which there is no capacity growth with increasing numbers of antennas. Furthermore, it is shown that this saturation point is dependent on the shape, size and orientation of the spatial volumes containing the antenna arrays along with the properties of the scattering environment. This result leads to the definition of an intrinsic capacity between separate spatial volumes in a continuous scattering environment, which is an upper limit to communication between the volumes that can not be increased with increasing numbers of antennas within. It is shown that there exists a fundamental limit to the information theoretic capacity between two continuous volumes in space, where using antenna arrays is simply one choice of implementation of a more general spatial signal processing underlying all wireless communication systems.

<p>Reading the daily financial news, it becomes quite obvious that hedge funds are receiving huge attention by financial analysts and politicians. Many people fear the influence of hedge funds on single companies as well as on the global economy.</p><p>This study does not judge the behavior of hedge funds. Instead, it focuses on the nature of hedge funds and their mystique image. Especially the common view of their market neutral performance is of interest, which is theoretically achieved through the use of derivatives and short positions. In this thesis the feature of market neutrality is investigated in depth, since it can improve the overall performance of investors’ portfolios in bull as well as in bear markets through diversification effects.</p><p>Therefore the hedge funds and their environment, the capital markets, are examined from an academically point of view by emphasizing on the following research questions:</p><p>1. Are hedge funds performing market neutral in bull and bear markets?</p><p>2. To what extent should they be included in optimal risky portfolios according to Modern Portfolio Theory and advanced performance measurement tools, considering their degree of market neutrality?</p><p>This study is based on extensive knowledge of financial and econometric theories. Capital market theories, modern portfolio theory, hedge fund data and econometric knowledge about time series analysis build the basis for further investigations and are necessary to understand the characteristics of hedge funds and hedge fund data.</p><p>In order to be able to deal with the shortcomings of hedge fund data, an analytical framework for the preparation of data is created that enables the authors to start with the analysis of these questions. The framework is applicable to all kinds of hedge funds presented in this thesis and enables the reader to test further hedge fund classes by himself.</p><p>In a quantitative study the created framework is applied to 2160 hedge funds of Barclays Hedge Fund Database, which builds the basis for analyzing market neutrality. Further input for the portfolio optimization consists of 19 hedge fund indices, which were provided by the Greenwich Alternative Investment Hedge Fund Database and 4 benchmark indices for the stock and bond market.</p><p>The analysis consists of two different parts. For the first research question various correlation and return matrices are constructed, which shall provide information about market neutrality of hedge funds. A correlation matrix also serves as important input for the portfolio analysis and therefore builds the basis for the analysis of the second research question. This shall provide some fundamental recommendations about the weighting of diverse hedge fund classes in optimal risky portfolios.</p><p>The conducted analysis demonstrated clearly the following findings:</p><p>1. Market neutrality has to be rejected for most hedge fund strategies. It is only attainable through strategies, which focus more on arbitrage and/or the bond market and therefore seems to be more a by-product than an actually provoked feature.</p><p>2. Only two strategies, equity short and convertible arbitrage, managed to beat the benchmark and to improve the overall performance of the portfolio when taking the specific return distribution of hedge funds into account.</p>

J.Y. Hayase. / "May 17, 1957." "This report is based on a thesis submitted to the Department of Electrical Engineering, M.I.T., May 20, 1957, in partial fulfillment of the requirements for the degree of Electrical Engineer." / Bibliography: p. 64-65. / U.S. Army Signal Corps Contract No. DA36-039-sc-64637 Dept. of the Army Task 3-99-06-108 Project 3-99-00-100

[by] L. Weinberg [and] L.G. Kraft. / Bibliography: p. 29. / Army Signal Corps Contract no. DA36-039 sc-100, Project no. 8-102B-0. Dept. of the Army Project No. 3-99-10-022.

Collecting microlymphatics play a vital role in promoting lymph flow from the initial lymphatics in the interstitial spaces to the large transport lymph ducts. In most tissues, the primary mechanism for producing this flow is the spontaneous contractions of the lymphatic wall. Individual units, known as lymphangion, are separated by valves that help prevent backflow when the vessel contracts, thus promoting flow through the lymphatic network. Lymphatic contractile activity is inhibited by flow in isolated lymphatics, however there are virtually no in situ measurements of lymph flow in these vessels. Initially, a high speed imaging system was set up to image in situ preparations at 500 fps. These images were then manually processed to extract information regarding lymphocyte velocity (-4 to 10 mm/sec), vessel diameter (25 to 165 um), and particle location. Fluid modeling was performed to obtain reasonable estimates of wall shear stress (-8 to 17 dynes/cm2). One of the difficulties encountered was the time consuming methods of manual particle tracking. Using previously captured images, an image correlation method was developed to automate lymphatic flow measurements and to track wall movements as the vessel contracts. Using this method the standard error of prediction for velocity measurements was 0.4 mm/sec and for diameter measurements it was 7.0 µm. It was found that the actual physical quantity being measured through this approach is somewhere between the spatially averaged velocity and the maximum velocity of a Poiseuille flow model.

Hedi HAMMAMI (2009). Genotype by Environment Interaction for Production Traits of Holsteins Using Two Countries as Model: Luxembourg and Tunisia (Doctoral thesis). Gembloux, Belgium, Gembloux Agricultural University, 170 p., 30 tabl., 16 fig. Summary. Under globalization, breeding organizations are selecting animals and exchanging germplasm across various environments. Ignoring genotype by environment interaction (G x E) may affect the efficiency of breeding strategies and limit outcomes from cooperation between breeding programs. Quantifying the effectiveness of indirect selection and effects of G x E for different breeds is therefore necessary. The objective of this thesis was to evaluate the magnitude of G x E for milk yield using Luxembourg and Tunisian Holstein populations. In fact, these two countries rely considerably on importation of superior genes from diverse origins for their breeding programs. This study needed records on both the genotype and the environment. In the first part of this thesis, genetic ties between the two populations were studied. Additive relationships and genetic similarity were important and genetic links have been strengthened with time which allowed the analysis of the phenotypic expression of daughters of common sires under each of these tow production environments. In the second part, genetic parameters for production traits of Tunisian Holsteins were estimated by a test-day random regression model (RRTD). Heritability estimates for 305-d milk, fat and protein yields were low to moderate (0.12 to 0.18) suspecting difficulties of high-producing cows to express their potential under limiting production conditions. In the third part, G x E for milk yield and persistency were investigated using character state models, where milk yield in each country was considered as a separate trait, and where the country border delimitation was designed as an environmental character state. A RRTD sire model was applied and was extended to a RRTD animal model. Significant G x E was detected for milk yield and persistency by both models. Large differences in genetic and permanent environmental variances between the two countries were observed. Genetic correlations for 305-d milk yield and persistency between Luxembourg and Tunisian Holsteins were 0.50 and 0.43 (sire model) and 0.60 and 0.36 (animal model). Moreover, low rank correlations obtained between estimated breeding values of common sires translate a significant re-ranking between the two environments. At the end of this thesis, a herd management (HM) parameter reflecting feeding and management intensity was defined. Three HM levels were identified in each country and G x E was investigated within- and across-environments. Significant G x E was detected between the Tunisian HM levels, whereas, only heterogeneous genetic variance for milk yield with limited re-ranking of sires across the three Luxembourg environments was observed. Overall, this thesis shows that under constraining environmental effects, selection for adaptive traits among economically valuable traits under their specific conditions is needed for low-input systems. When satisfactory feeding resources, management and husbandry practices are available, high degree environmental sensitivity is desired and the use of a high yielding breed may be encouraged.

This thesis is composed of three chapters. The first two chapters provides novel approaches for modeling and estimating the dependence structure for a large portfolio of assets using rating data. In both chapters a natural form of organizing a portfolio in terms of the levels of exposure to economic sectors and geographical regions, plays a key role in setting up the dependence structure. The last chapter investigates weather financial strategies that exploit sector or geographical heterogeneity in the asset space are relevant in terms of portfolio optimization. This is also done in a context of a large portfolio but with data on stock returns.

Agreement between raters on a categorical scale is not only a subject of scientific research but also a problem frequently encountered in practice. Whenever a new scale is developed to assess individuals or items in a certain context, inter-rater agreement is a prerequisite for the scale to be actually implemented in routine use. Cohen's kappa coeffcient is a landmark in the developments of rater agreement theory. This coeffcient, which operated a radical change in previously proposed indexes, opened a new field of research in the domain. In the first part of this work, after a brief review of agreement on a quantitative scale, the kappa-like family of agreement indexes is described in various instances: two raters, several raters, an isolated rater and a group of raters and two groups of raters. To quantify the agreement between two individual raters, Cohen's kappa coefficient (Cohen, 1960) and the intraclass kappa coefficient (Kraemer, 1979) are widely used for binary and nominal scales, while the weighted kappa coefficient (Cohen, 1968) is recommended for ordinal scales. An interpretation of the quadratic (Schuster, 2004) and the linear (Vanbelle and Albert, 2009c) weighting schemes is given. Cohen's kappa (Fleiss, 1971) and intraclass kappa (Landis and Koch, 1977c) coefficients were extended to the case where agreement is searched between several raters. Next, the kappa-like family of agreement coefficients is extended to the case of an isolated rater and a group of raters (Vanbelle and Albert, 2009a) and to the case of two groups of raters (Vanbelle and Albert, 2009b). These agreement coefficients are derived on a population-based model and reduce to the well-known Cohen's kappa coefficient in the case of two single raters. The proposed agreement indexes are also compared to existing methods, the consensus method and Schouten's agreement index (Schouten, 1982). The superiority of the new approach over the latter is shown. In the second part of the work, methods for hypothesis testing and data modeling are discussed. Firstly, the method proposed by Fleiss (1981) for comparing several independent agreement indexes is presented. Then, a bootstrap method initially developed by McKenzie et al. (1996) to compare two dependent agreement indexes, is extended to several dependent agreement indexes (Vanbelle and Albert, 2008). All these methods equally apply to the kappa coefficients introduced in the first part of the work. Next, regression methods for testing the effect of continuous and categorical covariates on the agreement between two or several raters are reviewed. This includes the weighted least-squares method allowing only for categorical covariates (Barnhart and Williamson, 2002) and a regression method based on two sets of generalized estimating equations. The latter method was developed for the intraclass kappa coefficient (Klar et al., 2000), Cohen's kappa coefficient (Williamson et al., 2000) and the weighted kappa coefficient (Gonin et al., 2000). Finally, a heuristic method, restricted to the case of independent observations, is presented (Lipsitz et al., 2001, 2003) which turns out to be equivalent to the generalized estimating equations approach. These regression methods are compared to the bootstrap method extended by Vanbelle and Albert (2008) but they were not generalized to agreement between a single rater and a group of raters nor between two groups of raters. / Sujet d'intenses recherches scientifiques, l'accord entre observateurs sur une échelle catégorisée est aussi un problème fréquemment rencontré en pratique. Lorsqu'une nouvelle échelle de mesure est développée pour évaluer des sujets ou des objets, l'étude de l'accord inter-observateurs est un prérequis indispensable pour son utilisation en routine. Le coefficient kappa de Cohen constitue un tournant dans les développements de la théorie sur l'accord entre observateurs. Ce coefficient, radicalement différent de ceux proposés auparavant, a ouvert de nouvelles voies de recherche dans le domaine. Dans la première partie de ce travail, après une brève revue des mesures d'accord sur une échelle quantitative, la famille des coefficients kappa est décrite dans différentes situations: deux observateurs, plusieurs observateurs, un observateur isolé et un groupe d'observateurs, et enfin deux groupes d'observateurs. Pour quantifier l'accord entre deux observateurs, le coefficient kappa de Cohen (Cohen, 1960) et le coefficient kappa intraclasse (Kraemer, 1979) sont largement utilisés pour les échelles binaires et nominales. Par contre, le coefficient kappa pondéré (Cohen, 1968) est recommandé pour les échelles ordinales. Schuster (2004) a donné une interprétation des poids quadratiques tandis que Vanbelle and Albert (2009c) se sont interessés aux poids linéaires. Les coefficients d'accord correspondant au coefficient kappa de Cohen (Fleiss, 1971) et au coefficient kappa intraclasse (Landis and Koch, 1977c) sont aussi donnés dans le cas de plusieurs observateurs. La famille des coefficients kappa est ensuite étendue au cas d'un observateur isolé et d'un groupe d'observateurs (Vanbelle and Albert, 2009a) et au cas de deux groupes d'observateurs (Vanbelle and Albert, 2009b). Les coefficients d'accord sont élaborés à partir d'un modèle de population et se réduisent au coefficient kappa de Cohen dans le cas de deux observateurs isolés. Les coefficients d'accord proposés sont aussi comparés aux méthodes existantes, la méthode du consensus et le coefficient d'accord de Schouten (Schouten, 1982). La supériorité de la nouvelle approche sur ces dernières est démontrée. Des méthodes qui permettent de tester des hypothèses et modéliser des coefficients d'accord sont abordées dans la seconde partie du travail. Une méthode permettant la comparaison de plusieurs coefficients d'accord indépendants (Fleiss, 1981) est d'abord présentée. Puis, une méthode basée sur le bootstrap, initialement développée par McKenzie et al. (1996) pour comparer deux coefficients d'accord dépendants, est étendue au cas de plusieurs coefficients dépendants par Vanbelle and Albert (2008). Pour finir, des méthodes de régression permettant de tester l'effet de covariables continues et catégorisées sur l'accord entre deux observateurs sont exposées. Ceci comprend la méthode des moindres carrés pondérés (Barnhart and Williamson, 2002), admettant seulement des covariables catégorisées, et une méthode de régression basée sur deux équations d'estimation généralisées. Cette dernière méthode a été développée dans le cas du coefficient kappa intraclasse (Klar et al., 2000), du coefficient kappa de Cohen (Williamson et al., 2000) et du coefficient kappa pondéré (Gonin et al., 2000). Enfin, une méthode heuristique, limitée au cas d'observations indépendantes, est présentée (Lipsitz et al., 2001, 2003). Elle est équivalente à l'approche par les équations d'estimation généralisées. Ces méthodes de régression sont comparées à l'approche par le bootstrap (Vanbelle and Albert, 2008) mais elles n'ont pas encore été généralisées au cas d'un observateur isolé et d'un groupe d'observateurs ni au cas de deux groupes d'observateurs. / Het bepalen van overeenstemming tussen beoordelaars voor categorische gegevens is niet alleen een kwestie van wetenschappelijk onderzoek, maar ook een probleem dat men veelvuldig in de praktijk tegenkomt. Telkens wanneer een nieuwe schaal wordt ontwikkeld om individuele personen of zaken te evalueren in een bepaalde context, is interbeoordelaarsovereenstemming een noodzakelijke voorwaarde vooraleer de schaal in de praktijk kan worden toegepast. Cohen's kappa coëfficiënt is een mijlpaal in de ontwikkeling van de theorie van interbeoordelaarsovereenstemming. Deze coëfficiënt, die een radicale verandering met de voorgaande indices inhield, opende een nieuw onderzoeksspoor in het domein. In het eerste deel van dit werk wordt, na een kort overzicht van overeenstemming voor kwantitatieve gegevens, de kappa-achtige familie van overeenstemmingsindices beschreven in verschillende gevallen: twee beoordelaars, verschillende beoordelaars, één geïsoleerde beoordelaar en een groep van beoordelaars, en twee groepen van beoordelaars. Om de overeenstemming tussen twee individuele beoordelaars te kwantificeren worden Cohen's kappa coëfficiënt (Cohen, 1960) en de intraklasse kappa coëfficiënt (Kraemer, 1979) veelvuldig gebruikt voor binaire en nominale gegevens, terwijl de gewogen Kappa coëfficiënt (Cohen, 1968) aangewezen is voor ordinale gegevens. Een interpretatie van de kwadratische (Schuster, 2004) en lineaire (Vanbelle and Albert, 2009c) weegschema's wordt gegeven. Overeenstemmingsindices die overeenkomen met Cohen's Kappa (Fleiss, 1971) en intraklasse-kappa (Landis and Koch, 1977c) coëfficiënten kunnen worden gebruikt om de overeenstemming tussen verschillende beoordelaars te beschrijven. Daarna wordt de familie van kappa-achtige overeenstemmingscoëfficiënten uitgebreid tot het geval van één geïsoleerde beoordelaar en een groep van beoordelaars (Vanbelle and Albert, 2009a) en tot het geval van twee groepen van beoordelaars (Vanbelle and Albert, 2009b). Deze overeenstemmingscoëfficiënten zijn afgeleid van een populatie-gebaseerd model en kunnen worden herleid tot de welbekende Cohen's coëfficiënt in het geval van twee individuele beoordelaars. De voorgestelde overeenstemmingsindices worden ook vergeleken met bestaande methodes, de consensusmethode en Schoutens overeenstemmingsindex (Schouten, 1982). De superioriteit van de nieuwe benadering over de laatstgenoemde wordt aangetoond. In het tweede deel van het werk worden hypothesetesten en gegevensmodellering besproken. Vooreerst wordt de methode voorgesteld door Fleiss (1981) om verschillende onafhankelijke overeenstemmingsindices te vergelijken, voorgesteld. Daarna wordt een bootstrapmethode, oorspronkelijk ontwikkeld door McKenzie et al. (1996) om twee onafhankelijke overeenstemmingsindices te vergelijken, uitgebreid tot verschillende afhankelijke overeenstemmingsindices (Vanbelle and Albert, 2008). Al deze methoden kunnen ook worden toegepast op de overeenstemmingsindices die in het eerste deel van het werk zijn beschreven. Ten slotte wordt een overzicht gegeven van regressiemethodes om het eect van continue en categorische covariabelen op de overeenstemming tussen twee of meer beoordelaars te testen. Dit omvat de gewogen kleinste kwadraten methode, die alleen werkt met categorische covariabelen (Barnhart and Williamson, 2002) en een regressiemethode gebaseerd op twee sets van gegeneraliseerde schattingsvergelijkingen. De laatste methode was ontwikkeld voor de intraklasse kappa coëfficiënt (Klar et al., 2000), Cohen's kappa coëfficiënt (Williamson et al., 2000) en de gewogen kappa coëfficiënt (Gonin et al., 2000). Ten slotte wordt een heuristische methode voorgesteld die alleen van toepassing is op het geval van onafhankelijk waarnemingen (Lipsitz et al., 2001, 2003). Ze blijkt equivalent te zijn met de benadering van de gegeneraliseerde schattingsvergelijkingen. Deze regressiemethoden worden vergeleken met de bootstrapmethode uitgebreid door Vanbelle and Albert (2008) maar werden niet veralgemeend tot de overeenstemming tussen een enkele beoordelaar en een groep van beoordelaars, en ook niet tussen twee groepen van beoordelaars.

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.