Mark Hammond

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Neural Integrator is a visual programming environment for prototyping and developing neural networks and other directed graph based programming models.

Neural Integrator is not yet stable. When it is stable, a download will be available on sourceforge.

Neural Integrator (NI) is more than just another neural net toolkit. It is a visual programming language and Integrated Development Environment (IDE) with an extremely open architecture. You can easily connect Neural Integrator projects to any input sources and output destinations and displays. For example: Excel COM support, JPEG or other image formats, and graphic displays are extensions, which, had we not included them, you could have written yourself! You can also design your own processing cells, or even wrap functions provided by other neural net products.

Neural Integrator is fast. The run-time engine and cell implementations are written in C++. All network data types are checked at "compile-time". Hence, python support does not decrease runtime performance.

Neural Integrator is flexible. You can design your own cell types in C++, python, or as sub-maps. You can customize the graphical user interface for your own cell types. Every aspect of Neural Integrator's GUI and abstraction functions is open-source and customizable.

Neural Integrator is intuitive to use. With interactive drag-and-drop cell controls, non-programmers can design sophisticated applications and leverage the work of programmers.

Neural Integrator is all about programming flexibility and integration. You can write custom cells that make calls to external libraries. You can use multi-language programming to control or embed working networks, even incorporate the networks into libraries. You can create (or contract for) a custom network compiler that will encode your networks for your specialized hardware.

In Neural Integrator creating support for Backpropagation Networks, Elman Networks, Jordan Networks, Functional Link Networks, Counterpropagation Networks, Multidirectional Associative Memories, Hopfield Memories, Markov Chains, Fuzzy Neural Systems, Recurrent Networks, and any variation or combination of these is a matter of describing the appropriate functionality as properties of a map and/or its component cells. In nearly every other neural net programming tool, implementing such variety would involve re-coding the tool itself. Any application that can be expressed with a discrete-time connectionist model is within Neural Integrator's scope.

Once written, such independently created modules can be made available for re-use through the Neural Integrator Open Source Network.

Preliminary documentation

Neural Network Links

Neural Networks in Hardware - Excellent overview. Who is using neural nets and why, hardware vs. software, etc.
Neural Networks at PPNL - Excellent site. Everything from Java demos to commercial applications, and lots of good links.

Current Status

The C kernel and Python interface are complete. The GUI (wxPython) is complete but needs some detail work. Module file saving and loading works (XML). The standard library needs lots of work.

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