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.
Highlights
The use of the Python programming language in computational neuroscience has been growing steadily during the past few years
The Modular toolkit for Data Processing (MDP) package4 contributes to this growing community a library of widely used data processing algorithms, and the possibility to combine them according to a pipeline analogy to build more complex data processing software
From the user’s perspective, MDP consists of a collection of supervised and unsupervised learning algorithms, and other data processing units that can be combined into data processing sequences and more complex feedforward network architectures
Summary
The use of the Python programming language in computational neuroscience has been growing steadily during the past few years. From the user’s perspective, MDP consists of a collection of supervised and unsupervised learning algorithms, and other data processing units (nodes) that can be combined into data processing sequences (flows) and more complex feedforward network architectures. Given a set of input data, MDP takes care of successively training or executing all nodes in the network. This allows the user to specify complex algorithms as a series of simpler data processing steps in a natural way. MDP is distributed under the open source LGPL license It has been written in the context of theoretical research in neuroscience, but was designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user’s side together with the reusability of the implemented nodes make it a useful educational tool
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
