| Title: |
The growing topology of the C. elegans connectome |
| Authors: |
Helm, Alec, Blevins, Ann S., Bassett, Danielle S. |
| Publication Year: |
2020 |
| Collection: |
Mathematics
Quantitative Biology |
| Subject Terms: |
Quantitative Biology - Neurons and Cognition, Mathematics - Algebraic Topology |
| More Details: |
Probing the developing neural circuitry in Caenorhabditis elegans has enhanced our understanding of nervous systems. The C. elegans connectome, like those of other species, is characterized by a rich club of densely connected neurons embedded within a small-world architecture. This organization of neuronal connections, captured by quantitative network statistics, provides insight into the system's capacity to perform integrative computations. Yet these network measures are limited in their ability to detect weakly connected motifs, such as topological cavities, that may support the systems capacity to perform segregated computations. We address this limitation by using persistent homology to track the evolution of topological cavities in the growing C. elegans connectome throughout neural development, and assess the degree to which the growing connectomes topology is resistant to biological noise. We show that the developing connectome topology is both relatively robust to changes in neuron birth times and not captured by similar growth models. Additionally, we quantify the consequence of a neurons specific birth time and ask if this metric tracks other biological properties of neurons. Our results suggest that the connectomes growing topology is a robust feature of the developing connectome that is distinct from other network properties, and that the growing topology is particularly sensitive to the exact birth times of a small set of predominantly motor neurons. By utilizing novel measurements that track biological features, we anticipate that our study will be helpful in the construction of more accurate models of neuronal development in C. elegans |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2101.00065 |
| Accession Number: |
edsarx.2101.00065 |
| Database: |
arXiv |
