Topology, geometry, and collective dynamics across brains, machines, and society.
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NPLab investigates how higher-order structure, topology, and geometry shape dynamics, computation, and information in complex systems—from neural and AI systems to animal collectives and society. We combine statistical mechanics, algebraic topology, information theory, and data-driven modelling, with an emphasis on open and reproducible research.
| Repository | What you will find |
|---|---|
| higher_order_LRG | Higher-order Laplacian renormalization for higher-order networks. |
| HOI_lenses_analysis | Code and data for analysing higher-order interaction structures and topological scaffolds in fMRI. |
| harmonic_degree | Theory, diagnostics, and benchmarks for dynamics-preserving network coarse-graining. |
| Modelling-TUS | Reproducible computational modelling of transcranial ultrasound stimulation. |
| social-asocial-learning | Network analysis of social and asocial learning in zebrafish. |
Most repositories accompany a paper or research project. Please use the repository's README for setup and citation information, and its issue tracker for code-specific questions.
