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ABSTRACT: Primates are very good at recognizing objects independent of viewing angle or retinal position, and they outperform existing computer vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment. An animal also needs to assess an object's position and relative rotational angle. We propose here a model that is able to extract object identity, position, and rotation angles. We demonstrate the model behavior on complex three-dimensional objects under translation and rotation in depth on a homogeneous background. A similar model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The framework for mathematical analysis of this earlier application carries over to the scenario of invariant object recognition. Thus, the simulation results can be explained analytically even for the complex high-dimensional data we employed.
Neural Computation 06/2011; 23(9):2289-323. · 1.88 Impact Factor
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Scholarpedia. 01/2011; 6:5282.
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ABSTRACT: Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA) network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.
PLoS Computational Biology 01/2010; 6(8). · 5.22 Impact Factor
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BMC Neuroscience. 01/2009;
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BMC Neuroscience. 01/2009;
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BMC Neuroscience. 01/2009;
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[show abstract]
[hide abstract]
ABSTRACT: Primates are very good at recognizing objects independently of viewing angle or retinal position and outperform existing computer
vision systems by far. But invariant object recognition is only one prerequisite for successful interaction with the environment.
An animal also needs to assess an object’s position and relative rotational angle. We propose here a model that is able to
extract object identity, position, and rotation angles, where each code is independent of all others. We demonstrate the model
behavior on complex three-dimensional objects under translation and in-depth rotation on homogeneous backgrounds. A similar
model has previously been shown to extract hippocampal spatial codes from quasi-natural videos. The rigorous mathematical
analysis of this earlier application carries over to the scenario of invariant object recognition.
09/2008: pages 961-970;
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ABSTRACT: Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool.
Frontiers in Neuroinformatics 02/2008; 2:8.
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Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I; 01/2008
