There is a theory by Hinton that the brain uses some variation of backprop for learning, and that it explains STDP. However backprop is difficult to use on rec

There is a theory by Hinton that the brain uses some variation of backprop for learning, and that it explains STDP. However backprop is difficult to use on recurrent signals, as opposed to feed forward ones. I have been thinking about methods to train RNNs in real time. As opposed to saving every time step and iterating over it later. It's a much more difficult problem because you lose the nice properties of backproping error signals over long time spans, and instead have to save any information that might be relevant in the future. One idea to do that is autoencoders. They can be done recurrently, compressing the information from previous time steps into a single vector. But they don't work very well, or at least no one has figured out how to do it well. There's also HTM which is a totally different system inspired by models of the neocortex, but it also isn't as good as LSTM yet.