[dwringer]

I'm not sure I'm able to provide a solid answer, but I would venture to say that to the extent a neuromorphic or neural "deep learning" network relies on an expensive training process, its inherent value becomes more-or-less unique to each specific implementation. Having the source code to build one's own implementation would not replace what could be hundreds of thousands of processor hours spent analyzing petabytes of data. In that case it would depend on whether the trained models were released under the open-source model, or only the underlying architecture.

[chienomi]

Thanks. This can explain the situation where most people are struggling to replicate a neat result in a paper.

[dwringer]

Well, I'm afraid of that I am not so sure. A well-designed paper should demonstrate what the training process was and use a publicly available dataset for training. There are many repositories of training data that exist specifically to provide a standard like this in various domains, so anyone following a paper should be able to use the same training process on the same data and get the same result. A paper presenting a result that cannot be obtained in this way can be informative but does not substitute for peer-reviewed research.

[dwringer]

Here is a discussion with some information on such data repositories as I mention in my other reply: http://academia.stackexchange.com/questions/85722/what-is-th...