  We formulate the search for phenomenological models of synaptic plasticity as
an optimization problem. We employ Cartesian genetic programming to evolve
biologically plausible human-interpretable plasticity rules that allow a given
network to successfully solve tasks from specific task families. While our
evolving-to-learn approach can be applied to various learning paradigms, here
we illustrate its power by evolving plasticity rules that allow a network to
efficiently determine the first principal component of its input distribution.
We demonstrate that the evolved rules perform competitively with known
hand-designed solutions. We explore how the statistical properties of the
datasets used during the evolutionary search influences the form of the
plasticity rules and discover new rules which are adapted to the structure of
the corresponding datasets.
