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2016


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Autofocusing-based correction of B0 fluctuation-induced ghosting

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

24th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), May 2016 (poster)

ei

link (url) [BibTex]

2016


link (url) [BibTex]


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PGO wave-triggered functional MRI: mapping the networks underlying synaptic consolidation

Logothetis, N. K., Murayama, Y., Ramirez-Villegas, J. F., Besserve, M., Evrard, H.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

ei

[BibTex]

[BibTex]


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Nonlinear functional causal models for distinguishing cause from effect

Zhang, K., Hyvärinen, A.

In Statistics and Causality: Methods for Applied Empirical Research, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

ei

[BibTex]

[BibTex]


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A cognitive brain–computer interface for patients with amyotrophic lateral sclerosis

Hohmann, M., Fomina, T., Jayaram, V., Widmann, N., Förster, C., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

In Brain-Computer Interfaces: Lab Experiments to Real-World Applications, 228(Supplement C):221-239, 8, Progress in Brain Research, (Editors: Damien Coyle), Elsevier, 2016 (incollection)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

ei

[BibTex]

[BibTex]


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Hippocampal neural events predict ongoing brain-wide BOLD activity

Besserve, M., Logothetis, N. K.

47th Annual Meeting of the Society for Neuroscience (Neuroscience), 2016 (poster)

ei

[BibTex]

[BibTex]

2015


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Diversity of sharp wave-ripples in the CA1 of the macaque hippocampus and their brain wide signatures

Ramirez-Villegas, J. F., Logothetis, N. K., Besserve, M.

45th Annual Meeting of the Society for Neuroscience (Neuroscience 2015), October 2015 (poster)

ei

link (url) [BibTex]

2015


link (url) [BibTex]


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Kernel methods in medical imaging

Charpiat, G., Hofmann, M., Schölkopf, B.

In Handbook of Biomedical Imaging, pages: 63-81, 4, (Editors: Paragios, N., Duncan, J. and Ayache, N.), Springer, Berlin, Germany, June 2015 (inbook)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Retrospective rigid motion correction of undersampled MRI data

Loktyushin, A., Babayeva, M., Gallichan, D., Krueger, G., Scheffler, K., Kober, T.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

ei

[BibTex]

[BibTex]


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Improving Quantitative Susceptibility and R2* Mapping by Applying Retrospective Motion Correction

Feng, X., Loktyushin, A., Deistung, A., Reichenbach, J. R.

23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine, ISMRM, June 2015 (poster)

ei

[BibTex]

[BibTex]


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Increasing the sensitivity of Kepler to Earth-like exoplanets

Foreman-Mackey, D., Hogg, D., Schölkopf, B., Wang, D.

Workshop: 225th American Astronomical Society Meeting 2015 , pages: 105.01D, 2015 (poster)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Calibrating the pixel-level Kepler imaging data with a causal data-driven model

Wang, D., Foreman-Mackey, D., Hogg, D., Schölkopf, B.

Workshop: 225th American Astronomical Society Meeting 2015 , pages: 258.08, 2015 (poster)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Disparity estimation from a generative light field model

Köhler, R., Schölkopf, B., Hirsch, M.

IEEE International Conference on Computer Vision (ICCV 2015), Workshop on Inverse Rendering, 2015, Note: This work has been presented as a poster and is not included in the workshop proceedings. (poster)

ei

[BibTex]

[BibTex]


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Justifying Information-Geometric Causal Inference

Janzing, D., Steudel, B., Shajarisales, N., Schölkopf, B.

In Measures of Complexity: Festschrift for Alexey Chervonenkis, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

ei

DOI [BibTex]

DOI [BibTex]

2008


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Variational Bayesian Model Selection in Linear Gaussian State-Space based Models

Chiappa, S.

International Workshop on Flexible Modelling: Smoothing and Robustness (FMSR 2008), 2008, pages: 1, November 2008 (poster)

ei

Web [BibTex]

2008


Web [BibTex]


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Towards the neural basis of the flash-lag effect

Ecker, A., Berens, P., Hoenselaar, A., Subramaniyan, M., Tolias, A., Bethge, M.

International Workshop on Aspects of Adaptive Cortex Dynamics, 2008, pages: 1, September 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Policy Learning: A Unified Perspective With Applications In Robotics

Peters, J., Kober, J., Nguyen-Tuong, D.

8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008), 8, pages: 10, July 2008 (poster)

Abstract
Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning al- gorithms from a common point of view, i.e, policy gradient algorithms, natural- gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.

ei

PDF [BibTex]

PDF [BibTex]


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Reinforcement Learning of Perceptual Coupling for Motor Primitives

Kober, J., Peters, J.

8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008), 8, pages: 16, July 2008 (poster)

Abstract
Reinforcement learning is a natural choice for the learning of complex motor tasks by reward-related self-improvement. As the space of movements is high-dimensional and continuous, a policy parametrization is needed which can be used in this context. Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamic systems motor primitives that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such a Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a human would hardly be able to learn this task. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for motor primitives.

ei

PDF [BibTex]

PDF [BibTex]


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Flexible Models for Population Spike Trains

Bethge, M., Macke, J., Berens, P., Ecker, A., Tolias, A.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 52, June 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Pairwise Correlations and Multineuronal Firing Patterns in the Primary Visual Cortex of the Awake, Behaving Macaque

Berens, P., Ecker, A., Subramaniyan, M., Macke, J., Hauck, P., Bethge, M., Tolias, A.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 48, June 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Visual saliency re-visited: Center-surround patterns emerge as optimal predictors for human fixation targets

Wichmann, F., Kienzle, W., Schölkopf, B., Franz, M.

Journal of Vision, 8(6):635, 8th Annual Meeting of the Vision Sciences Society (VSS), June 2008 (poster)

Abstract
Humans perceives the world by directing the center of gaze from one location to another via rapid eye movements, called saccades. In the period between saccades the direction of gaze is held fixed for a few hundred milliseconds (fixations). It is primarily during fixations that information enters the visual system. Remarkably, however, after only a few fixations we perceive a coherent, high-resolution scene despite the visual acuity of the eye quickly decreasing away from the center of gaze: This suggests an effective strategy for selecting saccade targets. Top-down effects, such as the observer's task, thoughts, or intentions have an effect on saccadic selection. Equally well known is that bottom-up effects-local image structure-influence saccade targeting regardless of top-down effects. However, the question of what the most salient visual features are is still under debate. Here we model the relationship between spatial intensity patterns in natural images and the response of the saccadic system using tools from machine learning. This allows us to identify the most salient image patterns that guide the bottom-up component of the saccadic selection system, which we refer to as perceptive fields. We show that center-surround patterns emerge as the optimal solution to the problem of predicting saccade targets. Using a novel nonlinear system identification technique we reduce our learned classifier to a one-layer feed-forward network which is surprisingly simple compared to previously suggested models assuming more complex computations such as multi-scale processing, oriented filters and lateral inhibition. Nevertheless, our model is equally predictive and generalizes better to novel image sets. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Analysis of Pattern Recognition Methods in Classifying Bold Signals in Monkeys at 7-Tesla

Ku, S., Gretton, A., Macke, J., Tolias, A., Logothetis, N.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 67, June 2008 (poster)

Abstract
Pattern recognition methods have shown that fMRI data can reveal significant information about brain activity. For example, in the debate of how object-categories are represented in the brain, multivariate analysis has been used to provide evidence of distributed encoding schemes. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success. In this study we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis and Gaussian naïve Bayes (GNB), using data collected at high field (7T) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no methods perform above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection, and outlier elimination.

ei

[BibTex]

[BibTex]


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New Frontiers in Characterizing Structure and Dynamics by NMR

Nilges, M., Markwick, P., Malliavin, TE., Rieping, W., Habeck, M.

In Computational Structural Biology: Methods and Applications, pages: 655-680, (Editors: Schwede, T. , M. C. Peitsch), World Scientific, New Jersey, NJ, USA, May 2008 (inbook)

Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both the structure and the dynamics of biological macromolecule in solution. Despite the maturity of the NMR method for structure determination, its application faces a number of challenges. The method is limited to systems of relatively small molecular mass, data collection times are long, data analysis remains a lengthy procedure, and it is difficult to evaluate the quality of the final structures. The last years have seen significant advances in experimental techniques to overcome or reduce some limitations. The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time–scales from picoseconds to seconds. NMR is unique in its ability to obtain dynamic information on an atomic scale. The experimental information on structure and dynamics is intricately mixed. It is however difficult to unite both structural and dynamical information into one consistent model, and protocols for the determination of structure and dynamics are performed independently. This chapter deals with the challenges posed by the interpretation of NMR data on structure and dynamics. We will first relate the standard structure calculation methods to Bayesian probability theory. We will then briefly describe the advantages of a fully Bayesian treatment of structure calculation. Then, we will illustrate the advantages of using Bayesian reasoning at least partly in standard structure calculations. The final part will be devoted to interpretation of experimental data on dynamics.

ei

Web [BibTex]

Web [BibTex]


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The role of stimulus correlations for population decoding in the retina

Schwartz, G., Macke, J., Berry, M.

Computational and Systems Neuroscience 2008 (COSYNE 2008), 5, pages: 172, March 2008 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Robot System for Biomimetic Navigation: From Snapshots to Metric Embeddings of View Graphs

Franz, MO., Stürzl, W., Reichardt, W., Mallot, HA.

In Robotics and Cognitive Approaches to Spatial Mapping, pages: 297-314, Springer Tracts in Advanced Robotics ; 38, (Editors: Jefferies, M.E. , W.-K. Yeap), Springer, Berlin, Germany, 2008 (inbook)

Abstract
Complex navigation behaviour (way-finding) involves recognizing several places and encoding a spatial relationship between them. Way-finding skills can be classified into a hierarchy according to the complexity of the tasks that can be performed [8]. The most basic form of way-finding is route navigation, followed by topological navigation where several routes are integrated into a graph-like representation. The highest level, survey navigation, is reached when this graph can be embedded into a common reference frame. In this chapter, we present the building blocks for a biomimetic robot navigation system that encompasses all levels of this hierarchy. As a local navigation method, we use scene-based homing. In this scheme, a goal location is characterized either by a panoramic snapshot of the light intensities as seen from the place, or by a record of the distances to the surrounding objects. The goal is found by moving in the direction that minimizes the discrepancy between the recorded intensities or distances and the current sensory input. For learning routes, the robot selects distinct views during exploration that are close enough to be reached by snapshot-based homing. When it encounters already visited places during route learning, it connects the routes and thus forms a topological representation of its environment termed a view graph. The final stage, survey navigation, is achieved by a graph embedding procedure which complements the topologic information of the view graph with odometric position estimates. Calculation of the graph embedding is done with a modified multidimensional scaling algorithm which makes use of distances and angles between nodes.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]

2007


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MR-Based PET Attenuation Correction: Method and Validation

Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Brady, M., Schölkopf, B., Pichler, B.

2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M16-6):1-2, November 2007 (poster)

Abstract
PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

ei

PDF PDF [BibTex]

2007


PDF PDF [BibTex]


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Estimating receptive fields without spike-triggering

Macke, J., Zeck, G., Bethge, M.

37th annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37(768.1):1, November 2007 (poster)

ei

Web [BibTex]

Web [BibTex]


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Evaluation of Deformable Registration Methods for MR-CT Atlas Alignment

Scheel, V., Hofmann, M., Rehfeld, N., Judenhofer, M., Claussen, C., Pichler, B.

2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M13-121):1, November 2007 (poster)

Abstract
Deformable registration methods are essential for multimodality imaging. Many different methods exist but due to the complexity of the deformed images a direct comparison of the methods is difficult. One particular application that requires high accuracy registration of MR-CT images is atlas-based attenuation correction for PET/MR. We compare four deformable registration algorithms for 3D image data included in the Open Source "National Library of Medicine Insight Segmentation and Registration Toolkit" (ITK). An interactive landmark based registration using MiraView (Siemens) has been used as gold standard. The automatic algorithms provided by ITK are based on the metrics Mattes mutual information as well as on normalized mutual information. The transformations are calculated by interpolating over a uniform B-Spline grid laying over the image to be warped. The algorithms were tested on head images from 10 subjects. We implemented a measure which segments head interior bone and air based on the CT images and l ow intensity classes of corresponding MRI images. The segmentation of bone is performed by individually calculating the lowest Hounsfield unit threshold for each CT image. The compromise is made by quantifying the number of overlapping voxels of the remaining structures. We show that the algorithms provided by ITK achieve similar or better accuracy than the time-consuming interactive landmark based registration. Thus, ITK provides an ideal platform to generate accurately fused datasets from different modalities, required for example for building training datasets for Atlas-based attenuation correction.

ei

PDF [BibTex]

PDF [BibTex]


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A time/frequency decomposition of information transmission by LFPs and spikes in the primary visual cortex

Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M., Logothetis, N., Panzeri, S.

37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37, pages: 1, November 2007 (poster)

ei

Web [BibTex]

Web [BibTex]


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Mining expression-dependent modules in the human interaction network

Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K.

BMC Bioinformatics, 8(Suppl. 8):S4, November 2007 (poster)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., Schölkopf, B.

Proceedings of the 10th International Conference on Discovery Science (DS 2007), 10, pages: 40-41, October 2007 (poster)

Abstract
While kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Support Vector Machine Learning for Interdependent and Structured Output Spaces

Altun, Y., Hofmann, T., Tsochantaridis, I.

In Predicting Structured Data, pages: 85-104, Advances in neural information processing systems, (Editors: Bakir, G. H. , T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, S. V. N. Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

ei

Web [BibTex]

Web [BibTex]


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Brisk Kernel ICA

Jegelka, S., Gretton, A.

In Large Scale Kernel Machines, pages: 225-250, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
Recent approaches to independent component analysis have used kernel independence measures to obtain very good performance in ICA, particularly in areas where classical methods experience difficulty (for instance, sources with near-zero kurtosis). In this chapter, we compare two efficient extensions of these methods for large-scale problems: random subsampling of entries in the Gram matrices used in defining the independence measures, and incomplete Cholesky decomposition of these matrices. We derive closed-form, efficiently computable approximations for the gradients of these measures, and compare their performance on ICA using both artificial and music data. We show that kernel ICA can scale up to much larger problems than yet attempted, and that incomplete Cholesky decomposition performs better than random sampling.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Training a Support Vector Machine in the Primal

Chapelle, O.

In Large Scale Kernel Machines, pages: 29-50, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007, This is a slightly updated version of the Neural Computation paper (inbook)

Abstract
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason to ignore this possibility. On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Approximation Methods for Gaussian Process Regression

Quiñonero-Candela, J., Rasmussen, CE., Williams, CKI.

In Large-Scale Kernel Machines, pages: 203-223, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Studying the effects of noise correlations on population coding using a sampling method

Ecker, A., Berens, P., Bethge, M., Logothetis, N., Tolias, A.

Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 21, September 2007 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Trading Convexity for Scalability

Collobert, R., Sinz, F., Weston, J., Bottou, L.

In Large Scale Kernel Machines, pages: 275-300, Neural Information Processing, (Editors: Bottou, L. , O. Chapelle, D. DeCoste, J. Weston), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

Abstract
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how nonconvexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Classifying Event-Related Desynchronization in EEG, ECoG and MEG signals

Hill, N., Lal, T., Tangermann, M., Hinterberger, T., Widman, G., Elger, C., Schölkopf, B., Birbaumer, N.

In Toward Brain-Computer Interfacing, pages: 235-260, Neural Information Processing, (Editors: G Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Joint Kernel Maps

Weston, J., Bakir, G., Bousquet, O., Mann, T., Noble, W., Schölkopf, B.

In Predicting Structured Data, pages: 67-84, Advances in neural information processing systems, (Editors: GH Bakir and T Hofmann and B Schölkopf and AJ Smola and B Taskar and SVN Vishwanathan), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

ei

Web [BibTex]

Web [BibTex]


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Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

Hinterberger, T., Nijboer, F., Kübler, A., Matuz, T., Furdea, A., Mochty, U., Jordan, M., Lal, T., Hill, J., Mellinger, J., Bensch, M., Tangermann, M., Widman, G., Elger, C., Rosenstiel, W., Schölkopf, B., Birbaumer, N.

In Toward Brain-Computer Interfacing, pages: 43-64, Neural Information Processing, (Editors: G. Dornhege and J del R Millán and T Hinterberger and DJ McFarland and K-R Müller), MIT Press, Cambridge, MA, USA, September 2007 (inbook)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Near-Maximum Entropy Models for Binary Neural Representations of Natural Images

Berens, P., Bethge, M.

Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 19, September 2007 (poster)

Abstract
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor scalability to high dimensions. In sensory coding, however, high-dimensional data is ubiquitous. Here, we introduce a new approach using a near-maximum entropy model, that makes this type of analysis feasible for very high-dimensional data---the model parameters can be derived in closed form and sampling is easy. We demonstrate its usefulness by studying a simple neural representation model of natural images. For the first time, we are able to directly compare predictions from a pairwise maximum entropy model not only in small groups of neurons, but also in larger populations of more than thousand units. Our results indicate that in such larger networks interactions exist that are not predicted by pairwise correlations, despite the fact that pairwise correlations explain the lower-dimensional marginal statistics extrem ely well up to the limit of dimensionality where estimation of the full joint distribution is feasible.

ei

PDF [BibTex]

PDF [BibTex]


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Learning the Influence of Spatio-Temporal Variations in Local Image Structure on Visual Saliency

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

10th T{\"u}binger Wahrnehmungskonferenz (TWK 2007), 10, pages: 1, July 2007 (poster)

Abstract
Computational models for bottom-up visual attention traditionally consist of a bank of Gabor-like or Difference-of-Gaussians filters and a nonlinear combination scheme which combines the filter responses into a real-valued saliency measure [1]. Recently it was shown that a standard machine learning algorithm can be used to derive a saliency model from human eye movement data with a very small number of additional assumptions. The learned model is much simpler than previous models, but nevertheless has state-of-the-art prediction performance [2]. A central result from this study is that DoG-like center-surround filters emerge as the unique solution to optimizing the predictivity of the model. Here we extend the learning method to the temporal domain. While the previous model [2] predicts visual saliency based on local pixel intensities in a static image, our model also takes into account temporal intensity variations. We find that the learned model responds strongly to temporal intensity changes ocurring 200-250ms before a saccade is initiated. This delay coincides with the typical saccadic latencies, indicating that the learning algorithm has extracted a meaningful statistic from the training data. In addition, we show that the model correctly predicts a significant proportion of human eye movements on previously unseen test data.

ei

Web [BibTex]

Web [BibTex]


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Better Codes for the P300 Visual Speller

Biessmann, F., Hill, N., Farquhar, J., Schölkopf, B.

G{\"o}ttingen Meeting of the German Neuroscience Society, 7, pages: 123, March 2007 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Do We Know What the Early Visual System Computes?

Bethge, M., Kayser, C.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 352, March 2007 (poster)

Abstract
Decades of research provided much data and insights into the mechanisms of the early visual system. Currently, however, there is great controversy on whether these findings can provide us with a thorough functional understanding of what the early visual system does, or formulated differently, of what it computes. At the Society for Neuroscience meeting 2005 in Washington, a symposium was held on the question "Do we know that the early visual system does", which was accompanied by a widely regarded publication in the Journal of Neuroscience. Yet, that discussion was rather specialized as it predominantly addressed the question of how well neural responses in retina, LGN, and cortex can be predicted from noise stimuli, but did not emphasize the question of whether we understand what the function of these early visual areas is. Here we will concentrate on this neuro-computational aspect of vision. Experts from neurobiology, psychophysics and computational neuroscience will present studies which approach this question from different viewpoints and promote a critical discussion of whether we actually understand what early areas contribute to the processing and perception of visual information.

ei

PDF [BibTex]

PDF [BibTex]


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Probabilistic Structure Calculation

Rieping, W., Habeck, M., Nilges, M.

In Structure and Biophysics: New Technologies for Current Challenges in Biology and Beyond, pages: 81-98, NATO Security through Science Series, (Editors: Puglisi, J. D.), Springer, Berlin, Germany, March 2007 (inbook)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Implicit Wiener Series for Estimating Nonlinear Receptive Fields

Franz, MO., Macke, JH., Saleem, A., Schultz, SR.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 1199, March 2007 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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3D Reconstruction of Neural Circuits from Serial EM Images

Maack, N., Kapfer, C., Macke, J., Schölkopf, B., Denk, W., Borst, A.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 1195, March 2007 (poster)

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PDF [BibTex]

PDF [BibTex]


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Identifying temporal population codes in the retina using canonical correlation analysis

Bethge, M., Macke, J., Gerwinn, S., Zeck, G.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 359, March 2007 (poster)

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PDF PDF [BibTex]

PDF PDF [BibTex]


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Bayesian Neural System identification: error bars, receptive fields and neural couplings

Gerwinn, S., Seeger, M., Zeck, G., Bethge, M.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 360, March 2007 (poster)

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PDF PDF [BibTex]

PDF PDF [BibTex]


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About the Triangle Inequality in Perceptual Spaces

Jäkel, F., Schölkopf, B., Wichmann, F.

Proceedings of the Computational and Systems Neuroscience Meeting 2007 (COSYNE), 4, pages: 308, February 2007 (poster)

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PDF Web [BibTex]

PDF Web [BibTex]