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We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learning
It is well known that artificial neural nets can be used as approximators of any continuous functions to any desired degree and therefore be used e.g. in high - speed, real-time process control. Nevertheless, for a given application and a given network architecture the non-trivial task remains to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation which are critical issues in VLSI and computer implementations of nontrivial tasks. In this paper the accuracy of the weights and the number of neurons are seen as general system parameters which determine the maximal approximation error by the absolute amount and the relative distribution of information contained in the network. We define as the error-bounded network descriptional complexity the minimal number of bits for a class of approximation networks which show a certain approximation error and achieve the conditions for this goal by the new principle of optimal information distribution. For two examples, a simple linear approximation of a non-linear, quadratic function and a non-linear approximation of the inverse kinematic transformation used in robot manipulator control, the principle of optimal information distribution gives the the optimal number of neurons and the resolutions of the variables, i.e. the minimal amount of storage for the neural net. Keywords: Kolmogorov complexity, e-Entropy, rate-distortion theory, approximation networks, information distribution, weight resolutions, Kohonen mapping, robot control.
We provide the first non-trivial result on dynamic breadth-first search (BFS) in external-memory: For general sparse undirected graphs of initially $n$ nodes and O(n) edges and monotone update sequences of either $\Theta(n)$ edge insertions or $\Theta(n)$ edge deletions, we prove an amortized high-probability bound of $O(n/B^{2/3}+\sort(n)\cdot \log B)$ I/Os per update. In contrast, the currently best approach for static BFS on sparse undirected graphs requires $\Omega(n/B^{1/2}+\sort(n))$ I/Os. 1998 ACM Subject Classification: F.2.2. Key words and phrases: External Memory, Dynamic Graph Algorithms, BFS, Randomization.
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.
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: 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.
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. ...
Poster presentation: Introduction The brain is a highly interconnected network of constantly interacting units. Understanding the collective behavior of these units requires a multi-dimensional approach. The results of such analyses are hard to visualize and interpret. Hence tools capable of dealing with such tasks become imperative. ....
The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor {kappa}B activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies.
Background Although current molecular clock methods offer greater flexibility in modelling historical evolutionary events, calibration of the clock with dates from the fossil record is still problematic for many groups. Here we implement several new approaches in molecular dating to estimate evolutionary ages of Lacertidae, an Old World family of lizards with a poor fossil record and uncertain phylogeny. Four different models of rate variation are tested in a new program for Bayesian phylogenetic analysis called TreeTime, based on a combination of mitochondrial and nuclear gene sequences. We incorporate paleontological uncertainty into divergence estimates by expressing multiple calibration dates as a range of probabilistic distributions. We also test the reliability of our proposed calibrations by exploring effects of individual priors on posterior estimates. Results According to the most reliable model, as indicated by Bayes factor comparison, modern lacertids arose shortly after the K/T transition and entered Africa about 45 million years ago, with the majority of their African radiation occurring in the Eocene and Oligocene. Our findings indicate much earlier origins for these clades than previously reported, and we discuss our results in light of paleogeographic trends during the Cenozoic. Conclusions This study represents the first attempt to estimate evolutionary ages of a specific group of reptiles exhibiting uncertain phylogenetic relationships, molecular rate variation and a poor fossil record. Our results emphasize the sensitivity of molecular divergence dates to fossil calibrations, and support the use of combined molecular data sets and multiple, well-spaced dates from the fossil record as minimum node constraints. The bioinformatics program used here, TreeTime, is publicly available, and we recommend its use for molecular dating of taxa faced with similar challenges.
Exported proteases of Helicobacter pylori (H. pylori) are potentially involved in pathogen-associated disorders leading to gastric inflammation and neoplasia. By comprehensive sequence screening of the H. pylori proteome for predicted secreted proteases, we retrieved several candidate genes. We detected caseinolytic activities of several such proteases, which are released independently from the H. pylori type IV secretion system encoded by the cag pathogenicity island (cagPAI). Among these, we found the predicted serine protease HtrA (Hp1019), which was previously identified in the bacterial secretome of H. pylori. Importantly, we further found that the H. pylori genes hp1018 and hp1019 represent a single gene likely coding for an exported protein. Here, we directly verified proteolytic activity of HtrA in vitro and identified the HtrA protease in zymograms by mass spectrometry. Overexpressed and purified HtrA exhibited pronounced proteolytic activity, which is inactivated after mutation of Ser205 to alanine in the predicted active center of HtrA. These data demonstrate that H. pylori secretes HtrA as an active protease, which might represent a novel candidate target for therapeutic intervention strategies.
We investigate unary regular languages and compare deterministic finite automata (DFA’s), nondeterministic finite automata (NFA’s) and probabilistic finite automata (PFA’s) with respect to their size. Given a unary PFA with n states and an e-isolated cutpoint, we show that the minimal equivalent DFA has at most n exp 1/2e states in its cycle. This result is almost optimal, since for any alpha < 1 a family of PFA’s can be constructed such that every equivalent DFA has at least n exp alpha/2e states. Thus we show that for the model of probabilistic automata with a constant error bound, there is only a polynomial blowup for cyclic languages. Given a unary NFA with n states, we show that efficiently approximating the size of a minimal equivalent NFA within the factor sqrt(n)/ln n is impossible unless P = NP. This result even holds under the promise that the accepted language is cyclic. On the other hand we show that we can approximate a minimal NFA within the factor ln n, if we are given a cyclic unary n-state DFA.
We present a biologically-inspired system for real-time, feed-forward object recognition in cluttered scenes. Our system utilizes a vocabulary of very sparse features that are shared between and within different object models. To detect objects in a novel scene, these features are located in the image, and each detected feature votes for all objects that are consistent with its presence. Due to the sharing of features between object models our approach is more scalable to large object databases than traditional methods. To demonstrate the utility of this approach, we train our system to recognize any of 50 objects in everyday cluttered scenes with substantial occlusion. Without further optimization we also demonstrate near-perfect recognition on a standard 3-D recognition problem. Our system has an interpretation as a sparsely connected feed-forward neural network, making it a viable model for fast, feed-forward object recognition in the primate visual system.
A new approach to optimize multilevel logic circuits is introduced. Given a multilevel circuit, the synthesis method optimizes its area while simultaneously enhancing its random pattern testability. The method is based on structural transformations at the gate level. New transformations involving EX-OR gates as well as Reed–Muller expansions have been introduced in the synthesis of multilevel circuits. This method is augmented with transformations that specifically enhance random-pattern testability while reducing the area. Testability enhancement is an integral part of our synthesis methodology. Experimental results show that the proposed methodology not only can achieve lower area than other similar tools, but that it achieves better testability compared to available testability enhancement tools such as tstfx. Specifically for ISCAS-85 benchmark circuits, it was observed that EX-OR gate-based transformations successfully contributed toward generating smaller circuits compared to other state-of-the-art logic optimization tools.
Channel routing is an NP-complete problem. Therefore, it is likely that there is no efficient algorithm solving this problem exactly.In this paper, we show that channel routing is a fixed-parameter tractable problem and that we can find a solution in linear time for a fixed channel width.We implemented our approach for the restricted layer model. The algorithm finds an optimal route for channels with up to 13 tracks within minutes or up to 11 tracks within seconds.Such narrow channels occur for example as a leaf problem of hierarchical routers or within standard cell generators.
We present a theoretical analysis of structural FSM traversal, which is the basis for the sequential equivalence checking algorithm Record & Play presented earlier. We compare the convergence behaviour of exact and approximative structural FSM traversal with that of standard BDD-based FSM traversal. We show that for most circuits encountered in practice exact structural FSM traversal reaches the fixed point as fast as symbolic FSM traversal, while approximation can significantly reduce in the number of iterations needed. Our experiments confirm these results.
We present the FPGA implementation of an algorithm [4] that computes implications between signal values in a boolean network. The research was performed as a masterrsquos thesis [5] at the University of Frankfurt. The recursive algorithm is rather complex for a hardware realization and therefore the FPGA implementation is an interesting example for the potential of reconfigurable computing beyond systolic algorithms. A circuit generator was written that transforms a boolean network into a network of small processing elements and a global control logic which together implement the algorithm. The resulting circuit performs the computation two orders of magnitudes faster than a software implementation run by a conventional workstation.
One of the most severe short-comings of currently available equivalence checkers is their inability to verify integer multipliers. In this paper, we present a bit level reverse-engineering technique that can be integrated into standard equivalence checking flows. We propose a Boolean mapping algorithm that extracts a network of half adders from the gate netlist of an addition circuit. Once the arithmetic bit level representation of the circuit is obtained, equivalence checking can be performed using simple arithmetic operations. Experimental results show the promise of our approach.
Considered are the classes QL (quasilinear) and NQL (nondet quasllmear) of all those problems that can be solved by deterministic (nondetermlnlsttc, respectively) Turmg machines in time O(n(log n) ~) for some k Effloent algorithms have time bounds of th~s type, it is argued. Many of the "exhausUve search" type problems such as satlsflablhty and colorabdlty are complete in NQL with respect to reductions that take O(n(log n) k) steps This lmphes that QL = NQL iff satisfiabdlty is m QL CR CATEGORIES: 5.25
In this paper we present a non-deterministic call-by-need (untyped) lambda calculus lambda nd with a constant choice and a let-syntax that models sharing. Our main result is that lambda nd has the nice operational properties of the standard lambda calculus: confluence on sets of expressions, and normal order reduction is sufficient to reach head normal form. Using a strong contextual equivalence we show correctness of several program transformations. In particular of lambdalifting using deterministic maximal free expressions. These results show that lambda nd is a new and also natural combination of non-determinism and lambda-calculus, which has a lot of opportunities for parallel evaluation. An intended application of lambda nd is as a foundation for compiling lazy functional programming languages with I/O based on direct calls. The set of correct program transformations can be rigorously distinguished from non-correct ones. All program transformations are permitted with the slight exception that for transformations like common subexpression elimination and lambda-lifting with maximal free expressions the involved subexpressions have to be deterministic ones.