You should look at the generalized recirculation algorithm, GeneRec, in O’Reilly and Munakata’s 2000 textbook. It essentially performs backprop, using only information local to each synapse. It gets information about error to each synapse by allowing the network to settle into a state constituting a “guess” as to the outcome, followed by a separate state that involves the correct output. Feedback and lateral connections ensure that the whole net is in a different state when correct output information is present. The learning rule then pushes weights toward the correct state, and away from the guess state. The learning rule at local synapses is very similar to the experimentally observed role of calcium influx at each synapse. Also, the algorithm seems to continue to work with very asymmetric connections, and would likely work without reciprocal connections between every individual unit.

This approach seems to solve the biological plausibility side of the error-driven learning puzzle. The remaining question, and one that must be answered by most forms of learning rule is this: where does the correct output information come from? I don’t think that anyone has fully thought through what information people actually use to learn from.

Although I cannot remember the reference I had seen a few years ago, I was able to find an article in the “Proceedings of the International Computer Music Association”. The reference is:

Gang, D., Lehmann, D. (1998) “Tuning a Neural Network for Harmonizing Melodies in Real-Time”. Proceedings of the International Computer Music Association.

Try http://citeseer.ist.psu.edu/gang98tuning.html for the text. The article describes the recurrent neural network and how it was trained.

I’m fairly sure the paper I had originally seen was in a journal in 2000 or so… if I run across it I will let you know.

-PL

can you give me a reference for your following claim?

«and even learning to play harmonic second voices in real-time.»

it would interest me.

thank you,
stefano