A multiple cause mixture model for unsupervised learning
Neural Computation
Self-organizing maps
Deterministic annealing EM algorithm
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Chaotic balanced state in a model of cortical circuits
Neural Computation
Adaptive resonance theory (ART)
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Feature extraction through LOCOCODE
Neural Computation
Swarm intelligence: from natural to artificial systems
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Distortion Invariant Object Recognition in the Dynamic Link Architecture
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Preintegration lateral inhibition enhances unsupervised learning
Neural Computation
Macrocolumns as Decision Units
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Biophysiologically plausible implementations of the maximum operation
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A Learning Rule to Model the Development of Orientation Selectivity in Visual Cortex
Neural Processing Letters
Hierarchial self-organization of minicolumnar receptive fields
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Topographic Product Models Applied to Natural Scene Statistics
Neural Computation
Learning sensory representations with intrinsic plasticity
Neurocomputing
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Neural Computation
Competition and multiple cause models
Neural Computation
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Dynamics of cortical columns – sensitive decision making
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LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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We study a dynamical model of processing and learning in the visual cortex, which reflects the anatomy of V1 cortical columns and properties of their neuronal receptive fields. Based on recent results on the fine-scale structure of columns in V1, we model the activity dynamics in subpopulations of excitatory neurons and their interaction with systems of inhibitory neurons. We find that a dynamical model based on these aspects of columnar anatomy can give rise to specific types of computations that result in self-organization of afferents to the column. For a given type of input, self-organization reliably extracts the basic input components represented by neuronal receptive fields. Self-organization is very noise tolerant and can robustly be applied to different types of input. To quantitatively analyze the system's component extraction capabilities, we use two standard benchmarks: the bars test and natural images. In the bars test, the system shows the highest noise robustness reported so far. If natural image patches are used as input, self-organization results in Gabor-like receptive fields. In quantitative comparison with in vivo measurements, we find that the obtained receptive fields capture statistical properties of V1 simple cells that algorithms such as independent component analysis or sparse coding do not reproduce.