133,803 research outputs found
Standard tone stability as a manipulation of precision in the oddball paradigm: Modulation of prediction error responses to fixed-probability deviants
Electrophysiological sensory deviance detection signals, such as the mismatch negativity (MMN), have been interpreted from the predictive coding framework as manifestations of prediction error (PE). From a frequentist perspective of the classic oddball paradigm, deviant stimuli are unexpected because of their low probability. However, the amount of PE elicited by a stimulus can be dissociated from its probability of occurrence: when the observer cannot make confident predictions, any event holds little surprise value, no matter how improbable. Here we tested the hypothesis that the magnitude of the neural response elicited to an improbable sound (D) would scale with the precision of the prediction derived from the repetition of another sound (S), by manipulating repetition stability. We recorded the Electroencephalogram (EEG) from 20 participants while passively listening to 4 types of isochronous pure tone sequences differing in the probability of the S tone (880 Hz) while holding constant the probability of the D tone [1,046 Hz; p(D) = 1/11]: Oddball [p(S) = 10/11]; High confidence (7/11); Low confidence (4/11); and Random (1/11). Tones of 9 different frequencies were equiprobably presented as fillers [p(S) C p(D) C p(F) = 1]. Using a mass-univariate non-parametric, cluster-based correlation analysis controlling for multiple comparisons, we found that the amplitude of the deviant-elicited ERP became more negative with increasing S probability, in a time-electrode window consistent with the MMN (ca. 120- 200 ms; frontal), suggesting that the strength of a PE elicited to an improbable event indeed increases with the precision of the predictive model
Detecting multineuronal temporal patterns in parallel spike trains
We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept
BINet: Multi-perspective Business Process Anomaly Classification
In this paper, we introduce BINet, a neural network architecture for
real-time multi-perspective anomaly detection in business process event logs.
BINet is designed to handle both the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets
A model of human event detection in multiple process monitoring situations
It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects
Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis
Segregating Event Streams and Noise with a Markov Renewal Process Model
DS and MP are supported by EPSRC Leadership Fellowship EP/G007144/1
Detection of recombination in DNA multiple alignments with hidden markov models
CConventional phylogenetic tree estimation methods assume that all sites in a DNA multiple alignment have the same evolutionary history. This assumption is violated in data sets from certain bacteria and viruses due to recombination, a process that leads to the creation of mosaic sequences from different strains and, if undetected, causes systematic errors in phylogenetic tree estimation. In the current work, a hidden Markov model (HMM) is employed to detect recombination events in multiple alignments of DNA sequences. The emission probabilities in a given state are determined by the branching order (topology) and the branch lengths of the respective phylogenetic tree, while the transition probabilities depend on the global recombination probability. The present study improves on an earlier heuristic parameter optimization scheme and shows how the branch lengths and the recombination probability can be optimized in a maximum likelihood sense by applying the expectation maximization (EM) algorithm. The novel algorithm is tested on a synthetic benchmark problem and is found to clearly outperform the earlier heuristic approach. The paper concludes with an application of this scheme to a DNA sequence alignment of the argF gene from four Neisseria strains, where a likely recombination event is clearly detected
A two-phase approach for detecting recombination in nucleotide sequences
Genetic recombination can produce heterogeneous phylogenetic histories within
a set of homologous genes. Delineating recombination events is important in the
study of molecular evolution, as inference of such events provides a clearer
picture of the phylogenetic relationships among different gene sequences or
genomes. Nevertheless, detecting recombination events can be a daunting task,
as the performance of different recombinationdetecting approaches can vary,
depending on evolutionary events that take place after recombination. We
recently evaluated the effects of postrecombination events on the prediction
accuracy of recombination-detecting approaches using simulated nucleotide
sequence data. The main conclusion, supported by other studies, is that one
should not depend on a single method when searching for recombination events.
In this paper, we introduce a two-phase strategy, applying three statistical
measures to detect the occurrence of recombination events, and a Bayesian
phylogenetic approach in delineating breakpoints of such events in nucleotide
sequences. We evaluate the performance of these approaches using simulated
data, and demonstrate the applicability of this strategy to empirical data. The
two-phase strategy proves to be time-efficient when applied to large datasets,
and yields high-confidence results.Comment: 5 pages, 3 figures. Chan CX, Beiko RG and Ragan MA (2007). A
two-phase approach for detecting recombination in nucleotide sequences. In
Hazelhurst S and Ramsay M (Eds) Proceedings of the First Southern African
Bioinformatics Workshop, 28-30 January, Johannesburg, 9-1
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