Building a small brain with a simple stochastic generative model

Abstract

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 C. 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. Validation of our models predictions of the synaptic connections using multiple reconstructions of adult worms suggests that our model identified the fundamental "backbone" of the connectivity graph. 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.