There is an interesting GSOC project proposal to implement Neural Network with backpropagation learning on Hadoop at apache mailing list. The idea is to create support for massivley parallel neural network system.
Apache Hadoop is a Java software framework that supports data-intensive distributed applications under a free license. It enables applications to work with thousands of nodes and petabytes of data. Hadoop was inspired by Google’s MapReduce and Google File System (GFS) papers.
Citation from the project proposal:
This architecture is inspired from that of the opensource Neuroph neural network framework (http://imgur.com/gDIOe.jpg). This design of the base architecture allows for great flexibility in deriving newer NNs and learning rules. All that needs to be done is to derive from the NeuralNetwork class, provide the method for network creation, create a new training method by deriving from LearningRule, and then add that learning rule to the network during creation. In addition, the API is very intuitive and easy to understand (in comparision to other NN frameworks like Encog and JOONE).
The Neuroph will support this project for sure.