Building the connectome of a small brain with a simple stochastic developmental generative model

The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits' structure and function. The ability to reconstruct neural circuits, and even entire nervous systems, at the neuron and synapse level, facilitates the study of the design principles of neural systems and their developmental plan. Here, we investigate the developing connectome of Caenorhabditis elegans using statistical generative models based on simple biological features: neuronal cell type, neuron birth time, cell body distance, reciprocity, and synaptic pruning. Our models accurately predict synapse existence, degree profiles of individual neurons, and statistics of small network motifs. Importantly, these models require a surprisingly small number of neuronal cell types, which we infer and characterize. We further show that to replicate the experimentally observed developmental path, multiple developmental epochs are necessary. Our model's predictions of the synaptic connections and their strength, using multiple reconstructions of adult worms, reflect that it identified much of the shared part of the connectivity graph. Thus, the accuracy of the generative statistical models we use here offers a general framework for studying how connectomes develop and the underlying principles of their design.