Klaus Obermayer

Bernstein Center for Computational Neuroscience Berlin, Berlín, Berlin, Germany

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Publications (268)325.83 Total impact

  • Dmytro Bielievtsov · Josef Ladenbauer · Klaus Obermayer
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    ABSTRACT: We address the problem of controlling first and second statistical moments for a general class of networks that exhibit nonlinear stochastic dynamics, taking advantage of their structural properties. We describe the dynamics of the moments by a deterministic system on which we apply a particular graph-based pinning control technique. A feedback controller is then developed for the network, which effectively is equivalent to pinning control of the deterministic moments system. Although the resulting controller requires full observability of the network's state, it is capable to stabilize and switch between metastable states by pinning only a subset of nodes. This subset of nodes is identified based on the connectivity matrix of the network only. Theoretical results are complemented with a concrete example of controlling a stochastic Hopfield network.
    No preview · Article · Dec 2015
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    ABSTRACT: In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.
    No preview · Article · Oct 2015 · Addiction Biology
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    Full-text · Dataset · Sep 2015
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    ABSTRACT: Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved using a recently developed filter-based template matching procedure. Using tetrodes with a 3-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of non-overlapping spikes, and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. Copyright © 2014, Journal of Neurophysiology.
    No preview · Article · Aug 2015 · Journal of Neurophysiology
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    Moritz Augustin · Josef Ladenbauer · Klaus Obermayer

    Preview · Conference Paper · Jul 2015
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    Florian Aspart · Josef Ladenbauer · Klaus Obermayer

    Preview · Conference Paper · Jul 2015
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    Sambu Seo · Johannes Mohr · Ningfei Li · Andreas Horn · Klaus Obermayer
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    ABSTRACT: Pairwise clustering methods are able to handle relational data, in which a set of objects is described via a matrix of pairwise (dis)similarities. Using the framework of source coding, it has been shown that pairwise clustering can be considered as entropy maximization problem under the constraint of keeping the distortion at a small value. This can be optimized via deterministic annealing. For the purpose of improving this optimization procedure, we have previously suggested two incremental pairwise clustering methods. However, they either only allow an even number of clusters, or cannot be applied to large proximity matrices. In this paper, we propose an incremental pairwise clustering method that resolves these issues. We compare the computational efficiency of the proposed algorithm to the previous incremental methods using simulations. Moreover, we apply the method to identify functionally connected brain networks by clustering a high-dimensional connectivity matrix obtained from resting state functional magnetic resonance imaging data.
    Full-text · Conference Paper · Jul 2015
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    Dataset: talk
    Brijnesh J. Jain · Klaus Obermayer

    Full-text · Dataset · Jun 2015
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    Dataset: talk
    Brijnesh J. Jain · Klaus Obermayer

    Full-text · Dataset · Jun 2015
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    ABSTRACT: This article reviews an emerging field that aims for autonomous reinforcement learning (RL) directly on sensor-observations. Straightforward end-to-end RL has recently shown remarkable success, but relies on large amounts of samples. As this is not feasible in robotics, we review two approaches to learn intermediate state representations from previous experiences: deep auto-encoders and slow-feature analysis. We analyze theoretical properties of the representations and point to potential improvements.
    Full-text · Article · Mar 2015
  • Y. Shen · W. Stannat · K. Obermayer
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    ABSTRACT: We introduce a unified framework for measuring risk in the context of Markov control processes with risk maps on general Borel spaces that generalize known concepts of risk measures in mathematical finance, operations research and behavioral economics. Within the framework, applying weighted norm spaces to incorporate also unbounded costs, we study two types of infinite-horizon risk-sensitive criteria, discounted total risk and average risk, and solve the associated optimization problems by dynamic programming. For the discounted case, we propose a new discount scheme, which is different from the conventional form but consistent with the existing literature, while for the average risk criterion, we state Lyapunov-type stability conditions that generalize known conditions for Markov chains to ensure the existence of solutions to the optimality equation.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
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    ABSTRACT: Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike wave-forms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filter-ing, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to com-pute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode record-ings. It may also be applicable to detection and classification problems of transient signals in general. Its application signifi-cantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching out-puts and show that they can be resolved with high accuracy.
    Full-text · Article · Feb 2015 · Journal of Computational Neuroscience
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    Full-text · Article · Feb 2015 · Journal of Computational Neuroscience
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    Wendelin Böhmer · Klaus Obermayer
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    ABSTRACT: This paper introduces a novel regression-algorithm based on factored functions. We analyze the regression problem with sample- and label-noise, and derive a regularization term from a Taylor approximation of the cost function. The regularization can be efficiently exploited by a greedy optimization scheme to learn factored basis functions during training. The novel algorithm performs competitively to Gaussian processes (GP), but is less susceptible to the curse of dimensionality. Learned linear factored functions (LFF) are on average represented by only 4-9 factored bases, which is considerably more compact than a GP.
    Full-text · Article · Dec 2014
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    ABSTRACT: Perceptual learning is the improvement in perceptual performance through training or exposure. Here, we used fMRI before and after extensive behavioral training to investigate the effects of perceptual learning on the recognition of objects under challenging viewing conditions. Objects belonged either to trained or untrained categories. Trained categories were further subdivided into trained and untrained exemplars and were coupled with high or low monetary rewards during training. After a 3-day training, object recognition was markedly improved. Whereas there was a considerable transfer of learning to untrained exemplars within categories, an enhancing effect of reward reinforcement was specific to trained exemplars. fMRI showed that hippocampus responses to both trained and untrained exemplars of trained categories were enhanced by perceptual learning and correlated with the effect of reward reinforcement. Our results suggest a key role of hippocampus in object recognition after perceptual learning.
    Full-text · Article · Sep 2014 · Journal of Cognitive Neuroscience
  • Johannes Mohr · Jong-Han Park · Klaus Obermayer
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    ABSTRACT: Humans are highly efficient at visual search tasks by focusing selective attention on a small but relevant region of a visual scene. Recent results from biological vision suggest that surfaces of distinct physical objects form the basic units of this attentional process. The aim of this paper is to demonstrate how such surface-based attention mechanisms can speed up a computer vision system for visual search. The system uses fast perceptual grouping of depth cues to represent the visual world at the level of surfaces. This representation is stored in short-term memory and updated over time. A top-down guided attention mechanism sequentially selects one of the surfaces for detailed inspection by a recognition module. We show that the proposed attention framework requires little computational overhead (about 11 ms), but enables the system to operate in real-time and leads to a substantial increase in search efficiency.
    No preview · Article · Sep 2014 · Neural Networks
  • Johannes Mohr · Sambu Seo · Klaus Obermayer
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    ABSTRACT: How can we test for group differences in multidimensional input patterns, such as functional magnetic resonance imaging measurements or gene expression values? One solution is to split the available data into training and test set, and to estimate the generalization accuracy of a classifier that predicts the group variable from the input pattern. If this lies significantly above chance level, we can reject the null hypothesis of no association. This test is straightforward for balanced data, where all groups are equally frequent in the data set. However, data sets collected in observational studies are often imbalanced. Then accuracy is no longer a suitable measure of performance, and balanced accuracy should be used instead. In this paper, we give an overview on existing analytical tests and use the framework of prediction theory to derive a new test for the balanced accuracy of a classifier. We then use numerical simulations to evaluate the type I error rate and the power of two tests for imbalanced data.
    No preview · Conference Paper · Jul 2014
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    ABSTRACT: Background / Purpose: Perceptual learning is the improvement in a perceptual task through repeated training or exposure. Previous studies in perceptual learning have mainly focused on very simple stimuli (e.g. lines or gratings). In this behavioral study we investigated reward-dependent perceptual learning of complex object recognition at the threshold of visual awareness. Main conclusion: We find that the subjects' performance improved significantly more for trained compared to untrained categories, with an additional advantage for high-rewarded stimuli.
    Full-text · Conference Paper · Jun 2014
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    Frederike Kneer · Klaus Obermayer · Markus A. Dahlem
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    ABSTRACT: The effect of advection on the critical minimal speed of traveling waves is studied. Previous theoretical studies estimated the effect on the velocity of stable fast waves and predicted the existence of a critical advection strength below which propagating waves are not supported anymore. In this paper, the critical advection strength is calculated taking into account the unstable slow wave solution. Thereby, theoretical results predict, that advection can induce stable wave propagation in the non-excitable parameter regime, if the advection strength exceeds a critical value. In addition, an analytical expression for the advection-velocity relation of the unstable slow wave is derived. Predictions are confirmed numerically in a two-variable reaction-diffusion model.
    Full-text · Article · Apr 2014 · The European Physical Journal E
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    Yun Shen · Michael J Tobia · Tobias Sommer · Klaus Obermayer
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    ABSTRACT: We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman & Tversky, 1979), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.
    Full-text · Article · Apr 2014 · Neural Computation

Publication Stats

4k Citations
325.83 Total Impact Points

Institutions

  • 2005-2015
    • Bernstein Center for Computational Neuroscience Berlin
      Berlín, Berlin, Germany
  • 1970-2015
    • Technische Universität Berlin
      • School IV Electrical Engineering and Computer Science
      Berlín, Berlin, Germany
  • 2007
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 1995
    • The Rockefeller University
      New York, New York, United States
  • 1994
    • Bielefeld University
      • Faculty of Technology
      Bielefeld, North Rhine-Westphalia, Germany