Speaker
Description
The mammalian cerebral cortex displays a complex network architecture supporting high-level cognition, yet the developmental rules driving its formation remain poorly understood. Current computational models often rely on static templates or global optimization, failing to capture the physical morphogenesis of neural tissue. Here, we present a multi-scale generative model integrating tissue-level mechanics with local wiring rules to simulate the emergence of the cortical connectome. Our approach combines a Cellular Potts Model (CPM) for tissue dynamics with a biologically grounded connectivity algorithm. We simulate the "inside-out" cortical developmental program, where early-born neurons settle in deep layers and later-born neurons migrate past them to superficial positions [1]. This spatiotemporal process naturally creates distinct distance constraints and cytoarchitectonic profiles without a priori prescription. As neurons settle, they form connections via spatially constrained preferential attachment and lineage-based microcolumnar biases [2]. We demonstrate that realistic macro-scale network topology emerges solely from these local developmental interactions. Simulated networks exhibit heavy-tailed degree distributions, hierarchical modularity, and realistic reciprocal clustering coefficients. Crucially, we reproduce the Architectonic Type Principle [3], where connectivity profiles are predicted by laminar cytoarchitectonic similarity between areas. This suggests that brain network topology is not necessarily the result of global optimization balancing wiring cost and topological value, but may emerge from simple physical migration and wiring rules. This framework bridges developmental biology and network science, providing a platform to investigate how perturbations in physical growth lead to altered network phenotypes in neurodevelopmental disorders.