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Institute
Poster presentation: An important challenge in neuroscience is understanding how networks of neurons go about processing information. Synapses are thought to play an essential role in cellular information processing however quantitative and mathematical models of the underlying physiologic processes that occur at synaptic active zones are lacking. We are generating mathematical models of synaptic vesicle dynamics at a well-characterized model synapse, the Drosophila larval neuromuscular junction. This synapse's simplicity, accessibility to various electrophysiological recording and imaging techniques, and the genetic malleability intrinsic to Drosophila system make it ideal for computational and mathematical studies. We have employed a reductionist approach and started by modeling single presynaptic boutons. Synaptic vesicles can be divided into different pools; however, a quantitative understanding of their dynamics at the Drosophila neuromuscular junction is lacking [4]. We performed biologically realistic simulations of high and low release probability boutons [3] using partial differential equations (PDE) taking into account not only the evolution in time but also the spatial structure in two dimensions (the extension to three dimensions will be implemented soon). PDEs are solved using UG, a program library for the calculation of multi-dimensional PDEs solved using a finite volume approach and implicit time stepping methods leading to extended linear equation systems be solvedwith multi-grid methods [3,4]. Numerical calculations are done on multi-processor computers for fast calculations using different parameters in order to asses the biological feasibility of different models. In preliminary simulations, we modeled vesicle dynamics as a diffusion process describing exocytosis as Neumann streams at synaptic active zones. The initial results obtained with these models are consistent with experimental data. However, this should be regarded as a work in progress. Further refinements will be implemented, including simulations using morphologically realistic geometries which were generated from confocal scans of the neuromuscular junction using NeuRA (a Neuron Reconstruction Algorithm). Other parameters such as glutamate diffusion and reuptake dynamics, as well as postsynaptic receptor kinetics will be incorporated as well.
This paper gives a brief overview of computation models for data stream processing, and it introduces a new model for multi-pass processing of multiple streams, the so-called mp2s-automata. Two algorithms for solving the set disjointness problem with these automata are presented. The main technical contribution of this paper is the proof of a lower bound on the size of memory and the number of heads that are required for solving the set disjointness problem with mp2s-automata.
The selection of features for classification, clustering and approximation is an important task in pattern recognition, data mining and soft computing. For real-valued features, this contribution shows how feature selection for a high number of features can be implemented using mutual in-formation. Especially, the common problem for mutual information computation of computing joint probabilities for many dimensions using only a few samples is treated by using the Rènyi mutual information of order two as computational base. For this, the Grassberger-Takens corre-lation integral is used which was developed for estimating probability densities in chaos theory. Additionally, an adaptive procedure for computing the hypercube size is introduced and for real world applications, the treatment of missing values is included. The computation procedure is accelerated by exploiting the ranking of the set of real feature values especially for the example of time series. As example, a small blackbox-glassbox example shows how the relevant features and their time lags are determined in the time series even if the input feature time series determine nonlinearly the output. A more realistic example from chemical industry shows that this enables a better ap-proximation of the input-output mapping than the best neural network approach developed for an international contest. By the computationally efficient implementation, mutual information becomes an attractive tool for feature selection even for a high number of real-valued features.
Ambiguity and communication
(2009)
The ambiguity of a nondeterministic finite automaton (NFA) N for input size n is the maximal number of accepting computations of N for an input of size n. For all k, r 2 N we construct languages Lr,k which can be recognized by NFA's with size k poly(r) and ambiguity O(nk), but Lr,k has only NFA's with exponential size, if ambiguity o(nk) is required. In particular, a hierarchy for polynomial ambiguity is obtained, solving a long standing open problem (Ravikumar and Ibarra, 1989, Leung, 1998).
We propose a variation of online paging in two-level memory systems where pages in the fast cache get modified and therefore have to be explicitly written back to the slow memory upon evictions. For increased performance, up to alpha arbitrary pages can be moved from the cache to the slow memory within a single joint eviction, whereas fetching pages from the slow memory is still performed on a one-by-one basis. The main objective in this new alpha-paging scenario is to bound the number of evictions. After providing experimental evidence that alpha-paging can adequately model flash-memory devices in the context of translation layers we turn to the theoretical connections between alpha-paging and standard paging. We give lower bounds for deterministic and randomized alpha-paging algorithms. For deterministic algorithms, we show that an adaptation of LRU is strongly competitive, while for the randomized case we show that by adapting the classical Mark algorithm we get an algorithm with a competitive ratio larger than the lower bound by a multiplicative factor of approximately 1.7.
Algorithms and data structures constitute the theoretical foundations of computer science and are an integral part of any classical computer science curriculum. Due to their high level of abstraction, the understanding of algorithms is of crucial concern to the vast majority of novice students. To facilitate the understanding and teaching of algorithms, a new research field termed "algorithm visualisation" evolved in the early 1980's. This field is concerned with innovating techniques and concepts for the development of effective algorithm visualisations for teaching, study, and research purposes. Due to the large number of requirements that high-quality algorithm visualisations need to meet, developing and deploying effective algorithm visualisations from scratch is often deemed to be an arduous, time-consuming task, which necessitates high-level skills in didactics, design, programming and evaluation. A substantial part of this thesis is devoted to the problems and solutions related to the automation of three-dimensional visual simulation of algorithms. The scientific contribution of the research presented in this work lies in addressing three concerns: - Identifying and investigating the issues related to the full automation of visual simulations. - Developing an automation-based approach to minimising the effort required for creating effective visual simulations. - Designing and implementing a rich environment for the visualisation of arbitrary algorithms and data structures in 3D. The presented research in this thesis is of considerable interest to (1) researchers anxious to facilitate the development process of algorithm visualisations, (2) educators concerned with adopting algorithm visualisations as a teaching aid and (3) students interested in developing their own algorithm animations.
The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ...
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural models. In the present thesis, we introduce several recurrent network models of threshold units that combine spike timing dependent plasticity with homeostatic plasticity mechanisms like intrinsic plasticity or synaptic normalization. We investigate how these different forms of plasticity shape the dynamics and computational properties of recurrent networks. The networks receive input sequences composed of different symbols and learn the structure embedded in these sequences in an unsupervised manner. Information is encoded in the form of trajectories through a high-dimensional state space reminiscent of recent biological findings on cortical coding. We find that these self-organizing plastic networks are able to represent and "understand" the spatio-temporal patterns in their inputs while maintaining their dynamics in a healthy regime suitable for learning. The emergent properties are not easily predictable on the basis of the individual plasticity mechanisms at work. Our results underscore the importance of studying the interaction of different forms of plasticity on network behavior.
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.
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. ...