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Christoph von der Malsburg
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2015) 27 (5): 1005–1032.
Published: 01 May 2015
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Assuming that patterns in memory are represented as two-dimensional arrays of local features, just as they are in primary visual cortices, pattern recognition can take the form of elastic graph matching (Lades et al., 1993 ). Neural implementation of this may be based on preorganized fiber projections that can be activated rapidly with the help of control units (Wolfrum, Wolff, Lücke, & von der Malsburg, 2008 ). Each control unit governs a set of projection fibers that form part of a coherent mapping. We describe a mathematical model for the ontogenesis of the underlying connectivity based on a principle of network self-organization as described by the Häussler system (Häussler & von der Malsburg, 1983 ), modified to be sensitive to pattern similarity and to support formation of multiple mappings, each under the command of a control unit. The process takes the form of a soft-winner-take-all, where units compete for the representation of maps. We show simulations for invariant point-to-point and feature-to-feature mappings.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2011) 23 (11): 2770–2797.
Published: 01 November 2011
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We present a model for the emergence of ordered fiber projections that may serve as a basis for invariant recognition. After invariance transformations are self-organized, so-called control units competitively activate fiber projections for different transformation parameters. The model builds on a well-known ontogenetic mechanism, activity-based development of retinotopy, and it employs activity blobs of varying position and size to install different transformations. We provide a detailed analysis for the case of 1D input and output fields for schematic input patterns that shows how the model is able to develop specific mappings. We discuss results that show that the proposed learning scheme is stable for complex, biologically more realistic input patterns. Finally, we show that the model generalizes to 2D neuronal fields driven by simulated retinal waves.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2008) 20 (10): 2441–2463.
Published: 01 October 2008
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We describe a neural network able to rapidly establish correspondence between neural feature layers. Each of the network's two layers consists of interconnected cortical columns, and each column consists of inhibitorily coupled subpopulations of excitatory neurons. The dynamics of the system builds on a dynamic model of a single column, which is consistent with recent experimental findings. The network realizes dynamic links between its layers with the help of specialized columns that evaluate similarities between the activity distributions of local feature cell populations, are subject to a topology constraint, and can gate the transfer of feature information between the neural layers. The system can robustly be applied to natural images, and correspondences are found in time intervals estimated to be smaller than 100 ms in physiological terms.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2008) 20 (6): 1452–1472.
Published: 01 June 2008
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This letter presents an improved cue integration approach to reliably separate coherent moving objects from their background scene in video sequences. The proposed method uses a probabilistic framework to unify bottom-up and top-down cues in a parallel, “democratic” fashion. The algorithm makes use of a modified Bayes rule where each pixel's posterior probabilities of figure or ground layer assignment are derived from likelihood models of three bottom-up cues and a prior model provided by a top-down cue. Each cue is treated as independent evidence for figure-ground separation. They compete with and complement each other dynamically by adjusting relative weights from frame to frame according to cue quality measured against the overall integration. At the same time, the likelihood or prior models of individual cues adapt toward the integrated result. These mechanisms enable the system to organize under the influence of visual scene structure without manual intervention. A novel contribution here is the incorporation of a top-down cue. It improves the system's robustness and accuracy and helps handle difficult and ambiguous situations, such as abrupt lighting changes or occlusion among multiple objects. Results on various video sequences are demonstrated and discussed. (Video demos are available at http://organic.usc.edu:8376/∼tangx/neco/index.html .)
Journal Articles
Publisher: Journals Gateway
Neural Computation (2007) 19 (12): 3293–3309.
Published: 01 December 2007
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Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data of the primate visual system.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2006) 18 (6): 1441–1471.
Published: 01 June 2006
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We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2004) 16 (12): 2563–2575.
Published: 01 December 2004
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We present an analysis of the representation of images as the magnitudes of their transform with complex-valued Gabor wavelets. Such a representation is a model for complex cells in the early stage of visual processing and of high technical usefulness for image understanding, because it makes the representation insensitive to small local shifts. We show that if the images are band limited and of zero mean, then reconstruction from the magnitudes is unique up to the sign for almost all images.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2004) 16 (3): 501–533.
Published: 01 March 2004
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We study a model of the cortical macrocolumn consisting of a collection of inhibitorily coupled minicolumns. The proposed system overcomes several severe deficits of systems based on single neurons as cerebral functional units, notably limited robustness to damage and unrealistically large computation time. Motivated by neuroanatomical and neurophysiological findings, the utilized dynamics is based on a simple model of a spiking neuron with refractory period, fixed random excitatory interconnection within minicolumns, and instantaneous inhibition within one macrocolumn. A stability analysis of the system's dynamical equations shows that minicolumns can act as monolithic functional units for purposes of critical, fast decisions and learning. Oscillating inhibition (in the gamma frequency range) leads to a phase-coupled population rate code and high sensitivity to small imbalances in minicolumn inputs. Minicolumns are shown to be able to organize their collective inputs without supervision by Hebbian plasticity into selective receptive field shapes, thereby becoming classifiers for input patterns. Using the bars test, we critically compare our system's performance with that of others and demonstrate its ability for distributed neural coding.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2003) 15 (8): 1865–1896.
Published: 01 August 2003
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The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses spatiotemporal retinal filtering, which is very sensitive to changes in the visual input. We show that it is crucial for successful learning to use the correlation of the transient responses instead of the sustained ones. As a consequence, learning works best with video sequences of moving objects. The model addresses a special case of the fundamental question of what represents the necessary a priori knowledge the brain is equipped with at birth so that the self-organized process of structuring by experience can be successful.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2001) 13 (9): 2049–2074.
Published: 01 September 2001
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Sensory integration or sensor fusion—the integration of information from different modalities, cues, or sensors—is among the most fundamental problems of perception in biological and artificial systems. We propose a new architecture for adaptively integrating different cues in a self-organized manner. In Democratic Integration different cues agree on a result, and each cue adapts toward the result agreed on. In particular, discordant cues are quickly suppressed and recalibrated, while cues having been consistent with the result in the recent past are given a higher weight in the future. The architecture is tested in a face tracking scenario. Experiments show its robustness with respect to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, although all cues may be disrupted at one time or another.
Journal Articles
Publisher: Journals Gateway
Neural Computation (1993) 5 (5): 719–735.
Published: 01 September 1993
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A large attraction of neural systems lies in their promise of replacing programming by learning. A problem with many current neural models is that with realistically large input patterns learning time explodes. This is a problem inherent in a notion of learning that is based almost entirely on statistical estimation. We propose here a different learning style where significant relations in the input pattern are recognized and expressed by the unsupervised self-organization of dynamic links. The power of this mechanism is due to the very general a priori principle of conservation of topological structure. We demonstrate that style with a system that learns to classify mirror symmetric pixel patterns from single examples.
Journal Articles
Publisher: Journals Gateway
Neural Computation (1990) 2 (1): 94–106.
Published: 01 March 1990
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The goal of this paper is to show how to modify associative memory such that it can discriminate several stored patterns in a composite input and represent them simultaneously. Segmention of patterns takes place in the temporal domain, components of one pattern becoming temporally correlated with each other and anticorrelated with the components of all other patterns. Correlations are created naturally by the usual associative connections. In our simulations, temporal patterns take the form of oscillatory bursts of activity. Model oscillators consist of pairs of local cell populations connected appropriately. Transition of activity from one pattern to another is induced by delayed self-inhibition or simply by noise.