  A primary goal of neuroscience is to understand how nervous systems, or
assemblies of neural circuits, generate and control behavior. Testing and
refining our theories of neural control would be greatly facilitated if we
could reliably simulate an entire nervous system so we could replicate the
brain dynamics in response to any stimuli and different contexts. More
fundamentally, reconstructing or modeling a system is an important milestone in
understanding it, and so, simulating an entire nervous system is in itself one
of the goals, indeed dreams, of systems neuroscience. To do so requires us to
identify how each neuron's output depends on its inputs, within some nervous
system. This deconstruction, understanding function from input-output pairs,
falls into the realm of reverse engineering. Current efforts at reverse
engineering the brain focus on the mammalian nervous system, but these brains
are complex, allowing only recordings of tiny subsystems. Here we argue that
the time is ripe to embark on a concerted effort to reverse engineer a smaller
system and that the nematode C. elegans is the ideal candidate system. In
particular, the established and growing toolkit of optophysiology techniques
can non-invasively capture and control each neuron's activity and scale to
hundreds of thousands of experiments, across a large population of animals.
Data across populations and behaviors can be combined because across
individuals neuronal identities are largely conserved in form and function.
Modern machine-learning-based model training should then enable a simulation of
C. elegans' impressive breadth of brain states and behaviors. The ability to
reverse engineer an entire nervous system will benefit systems neuroscience as
well as the design of artificial intelligence systems, enabling fundamental
insights as well as new approaches for investigations of progressively larger
nervous systems.
