  Continuous adaptation allows survival in an ever-changing world. Adjustments
in the synaptic coupling strength between neurons are essential for this
capability, setting us apart from simpler, hard-wired organisms. How these
changes can be mathematically described at the phenomenological level, as so
called "plasticity rules", is essential both for understanding biological
information processing and for developing cognitively performant artificial
systems. We suggest an automated approach for discovering biophysically
plausible plasticity rules based on the definition of task families, associated
performance measures and biophysical constraints. By evolving compact symbolic
expressions we ensure the discovered plasticity rules are amenable to intuitive
understanding, fundamental for successful communication and human-guided
generalization. We successfully apply our approach to 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.
