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. 2009 Jan 8:2:8.
doi: 10.3389/neuro.11.008.2008. eCollection 2008.

Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework

Tiziano Zito  1 , Niko Wilbert, Laurenz Wiskott, Pietro Berkes
Affiliations

Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework

Tiziano Zito et al. Front Neuroinform. .

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.

Keywords: Modular toolkit for Data Processing; Python; computational neuroscience; machine learning.

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Figures

Figure 1
Figure 1
A simple denoising application.
Figure 2
Figure 2
Definition of a new node that removes the mean of the signal.
Figure 3
Figure 3
Example of feed-forward network topology.
Figure 4
Figure 4
Python code to reproduce the results in Wiskott (2003).
Figure 5
Figure 5
Chaotic time series generated by the logistic equation.
Figure 6
Figure 6
The real driving force and the driving force as estimated by SFA.
See this image and copyright information in PMC

References

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