If you can train a non-linear physical system with this method, in principle, you could also train real brains. You can't update the parameters of the brain, but you can inject signal. Assuming real brains to be black box functions for which you could learn a noisy estimator of gradients, it could be used for neural implants that supplement lost brain functionality, or a Matrix-like skill loading system.
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If you can train a non-linear physical system with this method, in principle, you could also train real brains. You can't update the parameters of the brain, but you can inject signal. Assuming real brains to be black box functions for which you could learn a noisy estimator of gradients, it could be used for neural implants that supplement lost brain functionality, or a Matrix-like skill loading system.
You need a differentiable forward model of the process, which is not available for the human brain.