[Comp-neuro] OrGanic Environment for Reservoir Computing (OGER)
toolbox v1.0 released
david.verstraeten at elis.ugent.be
Sun Oct 10 15:31:28 CEST 2010
We are glad to announce the first official release of OGER (OrGanic Environment for Reservoir computing), a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets.
OGER is available from http://organic.elis.ugent.be/oger .
OGER builds functionality on top of the well-known Modular toolkit for Data Processing (MDP). Through MDP, Oger provides:
• Easily building, training and using modular structures of learning algorithms
• A wide variety of state-of-the-art machine learning methods, such as PCA, ICA, SFA, RBMs, ..
The Oger toolbox adds functionality to this such as:
• Several additional nodes such as Reservoir Computing nodes, a ridge regression node, a Conditional RBM node and a perceptron node
• Easy parallelization on computing clusters
• GPU-based acceleration
• Cross-validation and grid-searching of large parameter spaces
• Processing of sequential or temporal datasets
• Recursive generation of sequences
• Gradient-based training of (deep) learning architectures
• Interface to the Speech Processing, Recognition, and Automatic Annotation Kit (SPRAAK)
• Interface to PyNN-compatible spiking neural network simulators
Several tutorials and example datasets are provided to demonstrate the capabilities of OGER. The tutorials include:
- Modeling of place-cells for a robot with a reservoir with SFA and ICA
- Classification of MNIST data using a Deep-Belief Network with perceptron readout
- Constructing and training a TRM (Temporal Reservoir Machine) modular architecture
The OGER toolbox has been funded by the EU FP7 project ORGANIC.
Dr. ir. David Verstraeten
Department of Electronics and Information Systems
Sint Pietersnieuwstraat 41
B-9000 Ghent, Belgium
Phone : +32 9 264 34 04
Fax: + 32 9 264 35 94
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