If you have a perfect simulation of it, you can just run it step by step and create a computation graph and go backwards through it. The algorithm used to do t

If you have a perfect simulation of it, you can just run it step by step and create a computation graph and go backwards through it. The algorithm used to do the optimization doesn't really matter. Use a GA or hillclimbing if you want. EDIT since you added more to your comment: >why you'd ever want to do that "tweak it bit by bit" thing to a brain. Human brains seem to catch on to ideas pretty quickly. So what? That's the process for creating an adversarial image. Does it matter how many steps it takes to create it? >They don't need to go back and forth on their synapses a million times until they learn to react to a stimulus. That's exactly how learning works in humans. Try to learn to juggle with just one or two tries. It takes thousands. And that's after you've spent years in your body learning how to coordinate your muscles and locate objects with your eyes and how physics works, etc. >Whatever the brain does is light years ahead of backprop, which is, all things considered, a pretty poor algorithm. I really really doubt that. There are a number of theories about how the brain might implement a variation backpropagation for learning. Hinton has one. Backpropagation is not a poor algorithm, it's probably close to optimal. No one has been able to come up with something better besides just little heuristic tweaks. It's very difficult to see how you could do so. It's so simple and elegant and general.