  Control is essential to the functioning of any neural system. Indeed, under
healthy conditions the brain must be able to continuously maintain a tight
functional control between the system's inputs and outputs. One may therefore
hypothesise that the brain's wiring is predetermined by the need to maintain
control across multiple scales, maintaining the stability of key internal
variables, and producing behaviour in response to environmental cues. Recent
advances in network control have offered a powerful mathematical framework to
explore the structure-function relationship in complex biological, social, and
technological networks, and are beginning to yield important and precise
insights for neuronal systems. The network control paradigm promises a
predictive, quantitative framework to unite the distinct datasets necessary to
fully describe a nervous system, and provide mechanistic explanations for the
observed structure and function relationships. Here, we provide a thorough
review of the network control framework as applied to C. elegans, in the style
of a FAQ. We present the theoretical, computational, and experimental aspects
of network control, and discuss its current capabilities and limitations,
together with the next likely advances and improvements. We further present the
Python code to enable exploration of control principles in a manner specific to
this prototypical organism.
