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

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.

[1]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[2]  Ian Foster,et al.  Designing and building parallel programs , 1994 .

[3]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[4]  Andreas Ziehe,et al.  TDSEP { an e(cid:14)cient algorithm for blind separation using time structure , 1998 .

[5]  Jean-Franois Cardoso.  High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

[6]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[7]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[8]  Laurenz Wiskott.  Estimating Driving Forces of Nonstationary Time Series with Slow Feature Analysis Laurenz Wiskott Institute for Theoretical Biology , 2003 .

[9]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Laurenz Wiskott,et al.  CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization , 2004, IEEE Transactions on Signal Processing.

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  P. Berkes.  Temporal slowness as an unsupervised learning principle , 2006 .

[13]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[14]  Laurenz Wiskott,et al.  Independent Slow Feature Analysis and Nonlinear Blind Source Separation , 2004, Neural Computation.

[15]  Laurenz Wiskott,et al.  Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells , 2007, PLoS Comput. Biol..

[16]  V. A. Solé,et al.  A multiplatform code for the analysis of energy-dispersive X-ray fluorescence spectra , 2007 .

[17]  Niko Wilbert,et al.  Invariant Object Recognition with Slow Feature Analysis , 2008, ICANN.

[18]  Alexander B. Wiltschko,et al.  Wavelet filtering before spike detection preserves waveform shape and enhances single-unit discrimination , 2008, Journal of Neuroscience Methods.