Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes.

ABSTRACT: We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexit

SUBMITTER: Egas Santander D 

PROVIDER: S-EPMC11751574 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature