Durchblick im neuronalen Konzert : mit statistischen Methoden interpretieren Mathematiker neurowissenschaftliche Daten

Informationsverarbeitung im Gehirn basiert auf dem koordinierten Zusammenwirken von Milliarden von Nervenzellen. Um diese Codes zu entschlüsseln, sind komplexe Verfahren experimenteller Datenerhebung und theoretischer Datenanalyse notwendig. Denn auch wenn alle Zellen im selben Rhythmus agieren, kann sich jede auf ihre Art am Konzert beteiligen. Die verschiedenen Stimmen äußern sich in zeitlichen Mustern, die sich experimentell kaum vom Rauschen unterscheiden lassen. Erst mithilfe statistischer Verfahren konnten winzige zeitliche Verzögerungen als nicht zufällig identifiziert werden

Stochastic models for near-synchronous neuronal firing activity

It is commonly agreed that cortical information processing is based on the electric discharges (spikes') of nerve cells. Evidence is accumulating which suggests that the temporal interaction among a large number of neurons can take place with high precision, indicating that the efficiency of cortical processing may depend crucially on the precise spike timing of many cells. This work focuses on two temporal properties of parallel spike trains that attracted growing interest in the recent years: In the first place, specific delays (phase offsets') between the firing times of two spike trains are investigated. In particular, it is studied whether small phase offsets can be identified with confidence between two spike trains that have the tendency to fire almost simultaneously. Second, the temporal relations between multiple spike trains are investigated on the basis of such small offsets between pairs of processes. Since the analysis of all delays among the firing activity of n neurons is extremely complex, a method is required with which this highly dimensional information can be collapsed in a straightforward manner such that the temporal interaction among a large number of neurons can be represented consistently in a single temporal map. Finally, a stochastic model is presented that provides a framework to integrate and explain the observed temporal relations that result from the previous analyses.Aktuelle neurophysiologische Studien liefern Hinweise darauf, dass neuronale Informationsverarbeitung auf Mechanismen basiert, die mit hoher zeitlicher Präzision ablaufen. In dieser Arbeit werden drei Ansätze vorgestellt, mit denen die zeitliche Koordination der Feueraktivität von n parallelen Spike Trains statistisch analysiert und modelliert werden kann. Der erste Teil stellt eine Methode vor, mit der eine spezifische Verzögerung (die Phase') zwischen zwei parallelen Spike Trains gemessen werden kann. Inbesondere wird die Genauigkeit untersucht, mit der die Phase bei solchen Spike Trains bestimmt werden kann, die die Tendenz haben, dahezu simultan zu feuern. Im zweiten Teil wird ein Modell vorgestellt, mit dessen Hilfe untersucht werden soll, ob sich die zwischen n Spike Trains paarweise gemessenen Phasen in einer konsistenten, niederdimensionalen Darstellung erfassen lassen, in welcher jedem Prozess ein Punkt auf der Zeitachse zugeordnet wird. Im dritten Teil schließlich wird ein stochastisches Modell für n parallele Spike Trains mit koordinierter rhythmischer Feueraktivität präsentiert, in dessen Rahmen die in den vorherigen Analysen beobachteten zeitlichen Beziehungen integriert und erklärt werden

A simple Hidden Markov Model for midbrain dopaminergic neurons

Poster presentation: Introduction Dopaminergic neurons in the midbrain show a variety of firing patterns, ranging from very regular firing pacemaker cells to bursty and irregular neurons. The effects of different experimental conditions (like pharmacological treatment or genetical manipulations) on these neuronal discharge patterns may be subtle. Applying a stochastic model is a quantitative approach to reveal these changes. ..

A model for the joint evaluation of burstiness and regularity in oscillatory spike trains

Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions

Detection of single trial power coincidence for the identification of distributed cortical processes in a behavioral context

Poster presentation: The analysis of neuronal processes distributed across multiple cortical areas aims at the identification of interactions between signals recorded at different sites. Such interactions can be described by measuring the stability of phase angles in the case of oscillatory signals or other forms of signal dependencies for less regular signals. Before, however, any form of interaction can be analyzed at a given time and frequency, it is necessary to assess whether all potentially contributing signals are present. We have developed a new statistical procedure for the detection of coincident power in multiple simultaneously recorded analog signals, allowing the classification of events as 'non-accidental co-activation'. This method can effectively operate on single trials, each lasting only for a few seconds. Signals need to be transformed into time-frequency space, e.g. by applying a short-time Fourier transformation using a Gaussian window. The discrete wavelet transform (DWT) is used in order to weight the resulting power patterns according to their frequency. Subsequently, the weighted power patterns are binarized via applying a threshold. At this final stage, significant power coincidence is determined across all subgroups of channel combinations for individual frequencies by selecting the maximum ratio between observed and expected duration of co-activation as test statistic. The null hypothesis that the activity in each channel is independent from the activity in every other channel is simulated by independent, random rotation of the respective activity patterns. We applied this procedure to single trials of multiple simultaneously sampled local field potentials (LFPs) obtained from occipital, parietal, central and precentral areas of three macaque monkeys. Since their task was to use visual cues to perform a precise arm movement, co-activation of numerous cortical sites was expected. In a data set with 17 channels analyzed, up to 13 sites expressed simultaneous power in the range between 5 and 240 Hz. On average, more than 50% of active channels participated at least once in a significant power co-activation pattern (PCP). Because the significance of such PCPs can be evaluated at the level of single trials, we are confident that this procedure is useful to study single trial variability with sufficient accuracy that much of the behavioral variability can be explained by the dynamics of the underlying distributed neuronal processes

Analysis of higher-order coincident activity in multiple arallel processes

The synchronization of neuronal firing activity is considered an important mechanism in cortical information processing. The tendency of multiple neurons to synchronize their joint firing activity can be investigated with the 'unitary event' analysis (Grün, 1996). This method is based on the nullhypothesis of independent Bernoulli processes and can therefore not tell whether coincidences observed between more than two processes can be considered "genuine" higher- order coincidences or whether they might be caused by coincidences of lower order that coincide by chance ("chance coincidences"). In order to distinguish between genuine and chance coincidences, a parametric model of independent interaction processes (MIIP) is presented. In the framework of this model, Maximum-Likelihood estimates are derived for the firing rates of n single processes and for the rates with which genuine higher order correlations occur. The asymptotic normality of these estimates is used to derive their asymptotic variance and in order to investigate whether higher order coincidences can be considered genuine or whether they can be explained by chance coincidences. The empirical test power of this procedure for n=2 and n=3 processes and for finite analysis windows is derived with simulations and compared to the asymptotic values. Finally, the model is extended in order to allow for the analysis of correlations that are caused by jittered coincidences.Es wird vermutet, dass die Synchronisation der Feueraktivität vieler Neurone eine wichtige Rolle bei der kortikalen Informationsverarbeitung spielt. Um zu untersuchen, ob verschiedene Neurone die Tendenz haben, ihre Feueraktivität zu synchronisieren, wurde die Unitary Event Analyse (Grün, 1996) entwickelt. Diese basiert auf der Nullhypothese, dass die beobachteten Spike Trains unabhängige Bernoulli Prozesse sind, und kann daher nicht beantworten, ob vorhandene Korrelationen zwischen mehr als zwei Prozessen "echte" Korrelationen höherer Ordnung darstellen, oder ob es sich um "Scheinkorrelationen" handelt, die durch ein zufälliges Zusammentreffen von Koinzidenzen niedrigerer Ordnung verursacht werden. Um Scheinkorrelationen von echten Korrelationen höherer Ordnung zu unterscheiden, wird ein parametrisches Modell unabhängiger interagierender Prozesse (MIIP) vorgestellt. Im Rahmen des MIIP werden für n parallele Prozesse Maximum-Likelihood Schätzer bestimmt für die Feuerraten der einzelnen Prozesse sowie für die Raten, mit denen Koinzidenzen verschiedener Ordnungen auftreten. Die asymptotische Normalität der Schätzer wird verwendet, um ihre asymptotische Varianz zu bestimmen und statistisch zu untersuchen, ob Koinzidenzen höherer Ordnung durch Scheinkorrelationen zu erklären sind. Die Testpower dieses Verfahrens wird für n=2 und n=3 Prozesse und für endliche Datenstücke anhand von Simulationen untersucht und mit den asymptotischen Werten verglichen. Im letzten Teil wird das Modell erweitert, um auch solche Korrelationen in die Analyse einzuschließen, die auf unscharfen Koinzidenzen beruhen

Detection and localization of multiple rate changes in Poisson spike trains : poster presentation from Twentieth Annual Computational Neuroscience Meeting CNS*2011 Stockholm, Sweden, 23 - 28 July 2011

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. In statistical spike train analysis, stochastic point process models usually assume stationarity, in particular that the underlying spike train shows a constant firing rate (e.g. [1]). However, such models can lead to misinterpretation of the associated tests if the assumption of rate stationarity is not met (e.g. [2]). Therefore, the analysis of nonstationary data requires that rate changes can be located as precisely as possible. However, present statistical methods focus on rejecting the null hypothesis of stationarity without explicitly locating the change point(s) (e.g. [3]). We propose a test for stationarity of a given spike train that can also be used to estimate the change points in the firing rate. Assuming a Poisson process with piecewise constant firing rate, we propose a Step-Filter-Test (SFT) which can work simultaneously in different time scales, accounting for the high variety of firing patterns in experimental spike trains. Formally, we compare the numbers N1=N1(t,h) and N2=N2(t,h) of spikes in the time intervals (t-h,t] and (h,t+h]. By varying t within a fine time lattice and simultaneously varying the interval length h, we obtain a multivariate statistic D(h,t):=(N1-N2)/V(N1+N2), for which we prove asymptotic multivariate normality under homogeneity. From this a practical, graphical device to spot changes of the firing rate is constructed. Our graphical representation of D(h,t) (Figure 1A) visualizes the changes in the firing rate. For the statistical test, a threshold K is chosen such that under homogeneity, |D(h,t)|<K holds for all investigated h and t with probability 0.95. This threshold can indicate potential change points in order to estimate the inhomogeneous rate profile (Figure 1B). The SFT is applied to a sample data set of spontaneous single unit activity recorded from the substantia nigra of anesthetized mice. In this data set, multiple rate changes are identified which agree closely with visual inspection. In contrast to approaches choosing one fixed kernel width [4], our method has advantages in the flexibility of h