  Inspired by the natural nervous system, synaptic plasticity rules are applied
to train spiking neural networks with local information, making them suitable
for online learning on neuromorphic hardware. However, when such rules are
implemented to learn different new tasks, they usually require a significant
amount of work on task-dependent fine-tuning. This paper aims to make this
process easier by employing an evolutionary algorithm that evolves suitable
synaptic plasticity rules for the task at hand. More specifically, we provide a
set of various local signals, a set of mathematical operators, and a global
reward signal, after which a Cartesian genetic programming process finds an
optimal learning rule from these components. Using this approach, we find
learning rules that successfully solve an XOR and cart-pole task, and discover
new learning rules that outperform the baseline rules from literature.
