Before revision:  We demonstrate the flexibility of our approach by discovering efficient plasticity rules in typical learning scenarios  .
After revision:  We demonstrate the flexibility of our approach by discovering efficient plasticity rules in typical learning scenarios and discover previously unknown mechanisms for learning efficiently from rewards, recover efficient gradient-descent methods for learning from target signals, and uncover various functionally equivalent STDP-like rules with tuned homeostatic mechanisms .