Building the connectome of a small brain with a simple stochastic developmental 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 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 indiv

SUBMITTER: Richter O 

PROVIDER: S-EPMC12664000 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature