Computational generation of long-range axonal morphologies DOI Creative Commons
Adrien Berchet, Rémy Petkantchin, Henry Markram

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 18, 2024

Abstract Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between different regions of the brain. Recent advances in experimental techniques have yielded a substantial number whole-brain axonal reconstructions. While most previous computational generative models neurons predominantly focused on dendrites, generating realistic morphologies is challenging due their distinct targeting. In this study, we present novel algorithm for axon synthesis that combines algebraic topology with Steiner tree algorithm, an extension minimum spanning tree, generate both local long-range compartments axons. We demonstrate our computationally generated closely replicate data terms morphological properties. This approach enables generation biologically accurate span large distances connect multiple regions, advancing digital reconstruction Ultimately, opens up new possibilities large-scale in-silico simulations, research into function disorders.

Язык: Английский

Computational Generation of Long-range Axonal Morphologies DOI Creative Commons
Adrien Berchet, Rémy Petkantchin, Henry Markram

и другие.

Neuroinformatics, Год журнала: 2025, Номер 23(1)

Опубликована: Янв. 10, 2025

Abstract Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between different regions. Recent advances in experimental techniques have yielded a substantial number of whole-brain axonal reconstructions. While previous computational generative models neurons predominantly focused on dendrites, generating realistic morphologies is more challenging due their distinct targeting. In this study, we present novel algorithm for axon synthesis that combines algebraic topology with the Steiner tree algorithm, an extension minimum spanning tree, generate both local long-range compartments axons. We demonstrate our computationally generated closely replicate data terms morphological properties. This approach enables generation biologically accurate span large distances connect multiple regions, advancing digital reconstruction brain. Ultimately, opens up new possibilities large-scale in-silico simulations, research into function disorders.

Язык: Английский

Процитировано

0
Computational generation of long-range axonal morphologies DOI Creative Commons
Adrien Berchet, Rémy Petkantchin, Henry Markram

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 18, 2024

Abstract Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between different regions of the brain. Recent advances in experimental techniques have yielded a substantial number whole-brain axonal reconstructions. While most previous computational generative models neurons predominantly focused on dendrites, generating realistic morphologies is challenging due their distinct targeting. In this study, we present novel algorithm for axon synthesis that combines algebraic topology with Steiner tree algorithm, an extension minimum spanning tree, generate both local long-range compartments axons. We demonstrate our computationally generated closely replicate data terms morphological properties. This approach enables generation biologically accurate span large distances connect multiple regions, advancing digital reconstruction Ultimately, opens up new possibilities large-scale in-silico simulations, research into function disorders.

Язык: Английский

Процитировано

0