Learning algorithms remain a big issue in neuromorphic computing. So far, we have had some success with evolutionary optimization algorithms. The nice aspect of EO at the this stage of the game is that EO can reveal useful patterns and network features which could guide later learning systems.
I am working on a project right now which will require a more complex network interacting with many real world sensors, so it should be interesting to see how EO performs for such a problem. Our EO has been demonstrated to scale on parallel machines as large as ORNL's Titan, but I readily acknowledge that much more work needs to be done with learning algorithms.
The evolutionary process does not automatically prune elements. It is possible that previous non-useful features of a network become helpful at a later stage of evolution.
I am working on a project right now which will require a more complex network interacting with many real world sensors, so it should be interesting to see how EO performs for such a problem. Our EO has been demonstrated to scale on parallel machines as large as ORNL's Titan, but I readily acknowledge that much more work needs to be done with learning algorithms.