Andy Wuenscheandy AT ddlab DOT orgFlat 6, 3 Grape Street, London WC2 H8DX, UK
Visiting research fellow
DDLab mirror sites: Note: DDLab is no longer hosted at SFI as of July 2003. | 
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to support the DDLab project |
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DDLab latest beta release (DDLabm06) - Nov 2008
Many small but significant improvements and bug fixes. Vector PostScript is now available for most DDLab graphic output: space-time patterns, attractor basins, network graph and jump graph. This version will tie in with the new updated manual, which is in the pipeline. Any feedback is appreciated.
DDLab release -
Nov 2005
Multi-value DDLab version ddlabm05 has been updated. | 
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License and Registration | DDLab remains free shareware for personal, non-commercial users. If you have found the software useful, make a donation to the DDLab project by clicking the button near the top of the page. For a commercial or educational license, or for personal registration, click HERE. |
The DDLab Manual
Click here for the DDLab
Manual
Contents, Preface, Acknowledgments,
and links to versions of the first 4 chapters. |
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The DDLab GalleryThe DDLab Gallery is a collection of DDLab images and graphics, with captions, illustrating some of DDLab's features. The Gallery was started in Oct 1998. It will be continually added to and updated.
The figure on the right shows a new way of representing
a network as a graph which can be rearranged by dragging vertices.
This is a "scale free" RBN, n=100
with a power-law distribution of both k and out-degree. Lecture slidesAbout 80 of my lecture slides that have accumulated since 2006. Click here to see the slide pdf file in a new window - its a large file so might take a minute. You may use/copy these slides provided you reference myself and DDLab. |
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Attractor basins of discrete dynamical networks are objects in
space-time that link network states according to their
transitions. Click
Click here for a summary of idea.
Access to these objects provides insights into complexity,
chaos and emergent phenomena in cellular automata. In less ordered
networks (as well as CA), attractor basins show how a network is able to
categorize its state space, explaining what it is that constitutes
memory in a network.
What is DDLab?DDLab is interactive graphics software for researching discrete dynamical networks, relevant to the study of complexity, emergent phenomena, neural and bio-molecular networks - especially gene regulatory networks. DDlab is applied for research and education in university science, biology, informatics and complex systems departments, and for bio-technology research. A discrete dynamical network can have arbitrary connections and heterogeneous rules, and includes Cellular Autamata (CA), and "Random Boolean Networks" (RBN), where the "Boolean" atribute is extended to multi-value. Lattice dimensionality can be 1d, 2d (hex or square) or 3d. Many tools and functions are available for creating the network (its rules and wiring), setting the initial state, analyzing the dynamics, and amending parameters on-the-fly. An overview of DDLab and what it can do is provided in this pdf preprint. The program iterates the network forward to display space-time patterns, and also runs the network "backwards" to generate a pattern's predecessors and reconstruct its branching sub-tree of all ancestor patterns. For smaller networks, sub-trees, basins of attraction or the whole basin of attraction field can be reconstructed and displayed as directed graphs in real time. The DDLab Gallery shows examples. The network's parameters, and the graphics display and presentation options, can be flexibly set, reviewed and altered, including changes on-the-fly. A wide variety of measures, data, analysis and statistics are available. Learning/forgetting algorithms allow "sculpting" attractor basins to approach a desired scheme of hierarchical categorization. Read more about DDLab below:
Manual:
Contents, Preface, Acknowledgments,
and links to versions of the first 4 chapters. | 
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ReviewsReviews of DDLab
The entire book has been scanned and is availabe HERE in pdf format.
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DDLab versionsVersions of DDLab and documentation have been released as follows:
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