Klaus Obermayer

Technische Universität Berlin, Berlín, Berlin, Germany

Are you Klaus Obermayer?

Claim your profile

Publications (248)283.77 Total impact

  • [Show abstract] [Hide abstract]
    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.
    Journal of Neurophysiology 08/2015; DOI:10.1152/jn.00993.2014 · 3.04 Impact Factor
  • Source
    Sambu Seo · Johannes Mohr · Ningfei Li · Andreas Horn · Klaus Obermayer
    [Show abstract] [Hide abstract]
    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.
    International Joint Conference on Neural Networks 2015; 07/2015
  • Source
    Dataset: talk
    Brijnesh J. Jain · Klaus Obermayer
  • Source
    Dataset: talk
    Brijnesh J. Jain · Klaus Obermayer
  • Source
    [Show abstract] [Hide abstract]
    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.
    03/2015; DOI:10.1007/s13218-015-0356-1
  • Source
    [Show abstract] [Hide abstract]
    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.
    Journal of Computational Neuroscience 02/2015; 38(3):439-459. DOI:10.1007/s10827-015-0547-7 · 2.09 Impact Factor
  • Source
    Journal of Computational Neuroscience 02/2015; 38(3):439–459. DOI:10.1007/s10827-015-0555-7 · 2.09 Impact Factor
  • Source
    Wendelin Böhmer · Klaus Obermayer
    [Show abstract] [Hide abstract]
    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.
  • Johannes Mohr · Jong-Han Park · Klaus Obermayer
    [Show abstract] [Hide abstract]
    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.
    Neural Networks 09/2014; 60. DOI:10.1016/j.neunet.2014.08.010 · 2.08 Impact Factor
  • Source
    Yun Shen · Michael J Tobia · Tobias Sommer · Klaus Obermayer
    [Show abstract] [Hide abstract]
    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.
    Neural Computation 04/2014; 26(7). DOI:10.1162/NECO_a_00600 · 1.69 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Introduction: Brain stimulation is emerging as a fundamental tool in the clinical repertoire of a neurologist. Whereas invasive approaches are well established in clinical practice, non-invasive approaches are quickly gaining on importance. Independent of the type of stimulation, it is becoming remarkably clear that a better understanding of the neurophysiological mechanisms of interactions between patterns of stimulation and patterns of subject specific neural activity is necessary. The aim of this pilot study is to address if short periods of stimulation can entrain brain-rhythms. More explicitly, due to striking neurophysiological similarities between “photic driving” and “transorbital alternating current stimulation”, we compare short term photic- and electric stimulation. The hypothesis is that 30 seconds of bandwidth confined stimulation will evoke entrainment of the central alpha rhythm. Methods: To address this question, we stimulated 10 healthy subjects with retinofugal alternating current stimulation at 10 Hz for 30 seconds. In direct comparison, we induced steady-state visual evoked potentials at 10 Hz for 30 seconds. Sessions were applied in randomized order with baseline EEG recordings prior, during and after stimulation. EEG analyses were defined by clinical standards to identify “photic driving”. Results: In this framework we investigated: if a subject was susceptible to 10 Hz photic stimulation (DRIVING), if carry over effects exist for visual (VIS POST) and electric (ELC POST) stimulation. Results show that entrainment (DRIVING) could be induced and that alpha-entrainment persisted in both VIS POST and ELC POST conditions. All effects were significant in one-sided paired t-tests against baseline (p<0.05). Discussion: These findings show that short terms of brief stimulation can evoke significant entrainment of central rhythms. Remarkably, this was the case for both electric and photic stimulation. This provides a method to investigate quick changes in central rhythms induced by stimulation. One perspective is Brain-Computer-Interface driven stimulus optimization (DFG grant Nr: BR 1691/8-1).
    International Congress of Clinical Neurophysiology (ICCN), Berlin, Germany; 03/2014
  • Source
    Yun Shen · Wilhelm Stannat · Klaus Obermayer
    [Show abstract] [Hide abstract]
    ABSTRACT: We introduce the Lyapunov approach to optimal control problems of risk-sensitive Markov control processes on general Borel spaces equipped with risk maps, especially, with strictly convex risk maps like the entropic map. To ensure the existence and uniqueness of a solution to the associated nonlinear Poisson equation, we propose a new set of conditions: 1) Lyapunov-type conditions on both risk maps and cost functions that control the growth speed of iterations, and 2) Doeblin's conditions that generalize the known conditions for Markov chains. In the special case of the entropic map, we show that the above conditions can be replaced by the existence of a Lyapunov function, a local Doeblin's condition for the underlying Markov chain, and a growth condition for cost functions.
  • Source
    Josef Ladenbauer · Moritz Augustin · Klaus Obermayer
    [Show abstract] [Hide abstract]
    ABSTRACT: Many types of neurons exhibit spike rate adaptation, mediated by intrinsic slow K+ currents, which effectively inhibit neuronal responses. How these adaptation currents change the relationship between in vivo like fluctuating synaptic input, spike rate output, and the spike train statistics, however, is not well understood. In this computational study we show that an adaptation current that primarily depends on the subthreshold membrane voltage changes the neuronal input-output relationship (I-O curve) subtractively, thereby increasing the response threshold, and decreases its slope (response gain) for low spike rates. A spike-dependent adaptation current alters the I-O curve divisively, thus reducing the response gain. Both types of an adaptation current naturally increase the mean interspike interval (ISI), but they can affect ISI variability in opposite ways. A subthreshold current always causes an increase of variability while a spike-triggered current decreases high variability caused by fluctuation-dominated inputs and increases low variability when the average input is large. The effects on I-O curves match those caused by synaptic inhibition in networks with asynchronous irregular activity, for which we find subtractive and divisive changes caused by external and recurrent inhibition, respectively. Synaptic inhibition, however, always increases the ISI variability. We analytically derive expressions for the I-O curve and ISI variability, which demonstrate the robustness of our results. Furthermore, we show how the biophysical parameters of slow K+ conductances contribute to the two different types of an adaptation current and find that Ca2+ activated K+ currents are effectively captured by a simple spike-dependent description, while muscarine-sensitive or Na+ activated K+ currents show a dominant subthreshold component.
    Journal of Neurophysiology 03/2014; 111(5):939-953. DOI:10.1152/jn.00586.2013 · 3.04 Impact Factor
  • Source
    Robert Pröpper · Klaus Obermayer
    [Show abstract] [Hide abstract]
    ABSTRACT: Spyke Viewer is an open source application designed to help researchers analyze data from electrophysiological recordings or neural simulations. It provides a graphical data browser and supports finding and selecting relevant subsets of the data. Users can interact with the selected data using an integrated Python console or plugins. Spyke Viewer includes plugins for several common visualizations and allows users to easily extend the program by writing their own plugins. New plugins are automatically integrated with the graphical interface. Additional plugins can be downloaded and shared on a dedicated website.
    Frontiers in Neuroinformatics 11/2013; 7:26. DOI:10.3389/fninf.2013.00026
  • [Show abstract] [Hide abstract]
    ABSTRACT: According to the World Health Organization, about 2 billion people drink alcohol. Excessive alcohol consumption can result in alcohol addiction, which is one of the most prevalent neuropsychiatric diseases afflicting our society today. Prevention and intervention of alcohol binging in adolescents and treatment of alcoholism are major unmet challenges affecting our health-care system and society alike. Our newly formed German SysMedAlcoholism consortium is using a new systems medicine approach and intends (1) to define individual neurobehavioral risk profiles in adolescents that are predictive of alcohol use disorders later in life and (2) to identify new pharmacological targets and molecules for the treatment of alcoholism. To achieve these goals, we will use omics-information from epigenomics, genetics transcriptomics, neurodynamics, global neurochemical connectomes and neuroimaging (IMAGEN; Schumann et al. ) to feed mathematical prediction modules provided by two Bernstein Centers for Computational Neurosciences (Berlin and Heidelberg/Mannheim), the results of which will subsequently be functionally validated in independent clinical samples and appropriate animal models. This approach will lead to new early intervention strategies and identify innovative molecules for relapse prevention that will be tested in experimental human studies. This research program will ultimately help in consolidating addiction research clusters in Germany that can effectively conduct large clinical trials, implement early intervention strategies and impact political and healthcare decision makers.
    Addiction Biology 11/2013; 18(6):883-896. DOI:10.1111/adb.12109 · 5.93 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We analyze zero-lag and cluster synchrony of delay-coupled nonsmooth dynamical systems by extending the master stability approach, and apply this to networks of adaptive threshold-model neurons. For a homogeneous population of excitatory and inhibitory neurons we find (i) that subthreshold adaptation stabilizes or destabilizes synchrony depending on whether the recurrent synaptic excitatory or inhibitory couplings dominate, and (ii) that synchrony is always unstable for networks with balanced recurrent synaptic inputs. If couplings are not too strong, synchronization properties are similar for very different coupling topologies, i.e., random connections or spatial networks with localized connectivity. We generalize our approach for two subpopulations of neurons with nonidentical local dynamics, including bursting, for which activity-based adaptation controls the stability of cluster states, independent of a specific coupling topology.
    Physical Review E 10/2013; 88(4-1):042713. DOI:10.1103/PhysRevE.88.042713 · 2.33 Impact Factor
  • Source
    Moritz Augustin · Josef Ladenbauer · Klaus Obermayer
    BMC Neuroscience; 07/2013
  • Josef Ladenbauer · Moritz Augustin · Klaus Obermayer
    BMC Neuroscience 07/2013; 14(Suppl 1):P299-P299. DOI:10.1186/1471-2202-14-S1-P299 · 2.85 Impact Factor
  • Source
    BMC Neuroscience 07/2013; 14(1). DOI:10.1186/1471-2202-14-S1-P298 · 2.85 Impact Factor
  • Source
    Sein Schmidt · Michael Scholz · Klaus Obermayer · Stephan A Brandt
    [Show abstract] [Hide abstract]
    ABSTRACT: Brain stimulation is having remarkable impact on clinical neurology. Brain stimulation can modulate neuronal activity in functionally segregated circumscribed regions of the human brain. Polarity, frequency, and noise specific stimulation can induce specific manipulations on neural activity. In contrast to neocortical stimulation, deep-brain stimulation has become a tool that can dramatically improve the impact clinicians can possibly have on movement disorders. In contrast, neocortical brain stimulation is proving to be remarkably susceptible to intrinsic brain-states. Although evidence is accumulating that brain stimulation can facilitate recovery processes in patients with cerebral stroke, the high variability of results impedes successful clinical implementation. Interestingly, recent data in healthy subjects suggests that brain-state dependent patterned stimulation might help resolve some of the intrinsic variability found in previous studies. In parallel, other studies suggest that noisy "stochastic resonance" (SR)-like processes are a non-negligible component in non-invasive brain stimulation studies. The hypothesis developed in this manuscript is that stimulation patterning with noisy and oscillatory components will help patients recover from stroke related deficits more reliably. To address this hypothesis we focus on two factors common to both neural computation (intrinsic variables) as well as brain stimulation (extrinsic variables): noise and oscillation. We review diverse theoretical and experimental evidence that demonstrates that subject-function specific brain-states are associated with specific oscillatory activity patterns. These states are transient and can be maintained by noisy processes. The resulting control procedures can resemble homeostatic or SR processes. In this context we try to extend awareness for inter-individual differences and the use of individualized stimulation in the recovery maximization of stroke patients.
    Frontiers in Human Neuroscience 06/2013; 7:325. DOI:10.3389/fnhum.2013.00325 · 2.90 Impact Factor

Publication Stats

3k Citations
283.77 Total Impact Points

Institutions

  • 1970–2014
    • Technische Universität Berlin
      • • Department of Software Engineering and Theoretical Computer Science
      • • School IV Electrical Engineering and Computer Science
      Berlín, Berlin, Germany
  • 2005–2013
    • Bernstein Center for Computational Neuroscience Berlin
      Berlín, Berlin, Germany
  • 2007
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
  • 1995
    • Salk Institute
      La Jolla, California, United States
  • 1994
    • Bielefeld University
      • Faculty of Technology
      Bielefeld, North Rhine-Westphalia, Germany