Heiko Wersing, Stephan Kirstein, Michael Götting, Holger Brandl, Mark Dunn, Inna Mikhailova, Christian Goerick, Jochen Steil, Helge Ritter, Edgar Körner,
"Online Learning of Objects and Faces in an Integrated Biologically Motivated Architecture",
Proceedings of the 5th International Conference on Computer Vision Systems (ICVS), 2007.
Abstract
We present a biologically motivated integrated vision system that is capable of online learning of several objects and faces in a unified representation. The training is unconstrained in the sense that arbitrary objects can be freely presented in front of a stereo camera system and labeled by speech input. We combine biological principles such as appearance-based representation in topographical feature detection hierarchies and context-driven tra...
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Heiner Markert, Andreas Knoblauch, Günther Palm,
"Modelling of syntactical processing in the cortex",
BioSystems, vol. 89(1-3), pp. 300–315, 2007.
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Robert Cannon, Marc-Oliver Gewaltig, Padraig Gleeson, Upinder Bhalla, Hugo Cornelis, Michael Hines, Fredrick Howell, Eilif Muller, Joel Stiles, Stefan Wiles, Eric Schutter,
"Interoperability of Neuroscience Modeling Software: Current Status and Future Directions",
Neuroinformatics, vol. 5, pp. 127–138, 2007.
Abstract
Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperabi...
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Daniel Weiler, Volker Willert, Julian Eggert, Edgar Körner,
"A Probabilistic Method for Motion Pattern Segmentation",
Proceedings of the 2007 International Joint Conference on Neural Networks (IJCNN), 2007.
Abstract
In this paper we present an approach for probabilistic motion pattern segmentation. We combine level-set methods for image segmentation with motion estimations based on probability distribution functions (pdf’s) calculated at each image position. To this end, we extend a region based levelset framework to exploit the motion pdf’s. We then compare segmentation results of the pdf-based with those of opticalflow- based motion segmentation approa...
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Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff, Edward Tsang,
"Global multi-objective optimization via estimation of distribution algorithm with biased initialization and crossover",
Genetic and Evolutionary Computation Conference, 2007.
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Xavier Domont, Martin Heckmann, Heiko Wersing, Frank Joublin, Christian Goerick,
"A Hierarchical Model for Syllable Recognition",
European Symposium on Artificial Neural Networks (ESANN), pp. 573–578, 2007.
Abstract
Inspired by recent findings on the similarities between the primary auditory and visual cortex we propose a neural network for speech recognition based on a hierarchical feedforward architecture for visual object recognition. When using a Gammatone filterbank for the spectral analysis the resulting spectrograms of syllables can be interpreted as images. After a preprocessing enhancing the formants in the speech signal and a length normalization, ...
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Jochen Eppler, Hans Plesser, Abigail Morrison, Markus Diesmann, Marc-Oliver Gewaltig,
"Multithreaded and Distributed Simulation of Large Biological Neuronal Networks",
Recent Advances in Parallel Virtual Machine and Message Passing Interface, issue Volume 4757/2007, pp. 391-392, 2007.
Abstract
To understand the principles of information processing in the brain, we depend on models with more than 10^5 neurons and 10^9 connections. These networks can be described as graphs of threshold elements that exchange point events....
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Jochen Steil, Michael Götting, Heiko Wersing, Edgar Körner, Helge Ritter,
"Adaptive Scene-Dependent Filters for Segmentation and Online Learning of Visual Objects",
Neurocomputing, vol. 70, pp. 1235–1246, 2007.
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Sven Rebhan, Julian Eggert, Horst-Michael Groß, Edgar Körner,
"Sparse and Transformation-Invariant Hierarchical NMF",
Artificial Neural Networks, 17 th International Conference (ICANN), pp. 894–903, 2007.
Abstract
The hierarchical non-negative matrix factorization (HNMF) is a multilayer generative network for decomposing strictly positive data into strictly positive activations and base vectors in a hierarchical manner. However, the standard hierarchical NMF is not suited for overcomplete representations and does not code efficiently for transformations in the input data. Therefore we extend the standard HNMF by sparsity conditions and transformation-invar...
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Rüdiger Kupper, Andreas Knoblauch, Marc-Oliver Gewaltig, Ursula Körner, Edgar Körner,
"Simulations of signal flow in a functional model of the cortical column",
Neurocomputing, vol. 70, no. 10-12, pp. 1711-1716, 2007.
Abstract
We describe the simulation of a layered cortex model based on the cortical column as a generic local processor. It simulates the signal flow in the layers I–IV of a set of model columns across three hierarchical cortical areas. It demonstrates the fast formation of an initial stimulus hypothesis, and its subsequent refinement by inter-columnar communication. In this prototype simulation, we implement word recognition from a string of characters...
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