I was just thinking lately that brains rewire themselves all the time. That's how they learn. Neural networks, though, never do. There's a separate training sta

I was just thinking lately that brains rewire themselves all the time. That's how they learn. Neural networks, though, never do. There's a separate training stage that (from my understanding) runs the thing in reverse and modifies the weights to move the output closer to what is desired. After that, the weights are never updated. Also, yes, information always flows through a neural network in one direction, from inputs to outputs, then leaves it. Whether the inference result was correct or wrong, it doesn't have any effect on the weights. So I'm thinking that there is a need for some new kind of neural network architecture that can 1) update its own weights all the time, not just when training, and 2) propagate information in reverse direction from outputs to inputs, or have "backwards" connections between layers. Maybe have a set of outputs that tell the system whether and how the NN wants its own weights updated?