Simple Rules Can Build a Complex Brain

Biological neural networks are marvels of organisation, shaped by genetics, biophysical constraints, and chance. But how exactly does a collection of cells structure itself into a functioning system? To answer this, researchers investigated the developing connectome—the complete map of neural connections—of the roundworm Caenorhabditis elegans.

Using statistical generative models, the team discovered that the worm’s neural architecture could be accurately predicted using a handful of simple features. These included the specific neuronal cell type, the time of the neuron's birth, and the physical distance between cell bodies. The models also accounted for synaptic pruning, a process where weak or unnecessary connections are removed.

Significantly, the researchers found that accurate modelling required a surprisingly small number of cell types, though it did necessitate multiple distinct developmental epochs to replicate the path observed in nature. This work suggests that the complex design of a nervous system can emerge from a foundation of simple, stochastic rules, offering a general framework for understanding how brains develop.