  Jellyfish nerve nets provide insight into the origins of nervous systems, as
both their taxonomic position and their evolutionary age imply that jellyfish
resemble some of the earliest neuron-bearing, actively-swimming animals. Here
we develop the first neuronal network model for the nerve nets of jellyfish.
Specifically, we focus on the moon jelly Aurelia aurita and the control of its
energy-efficient swimming motion. The proposed single neuron model disentangles
the contributions of different currents to a spike. The network model
identifies factors ensuring non-pathological activity and suggests an
optimization for the transmission of signals. After modeling the jellyfish's
muscle system and its bell in a hydrodynamic environment, we explore the
swimming elicited by neural activity. We find that different delays between
nerve net activations lead to well-controlled, differently directed movements.
Our model bridges the scales from single neurons to behavior, allowing for a
comprehensive understanding of jellyfish neural control.
