Connectome structure constrains neural activity across scales
- 1Scuola Superiore Meridionale
Abstract
Connectomes can be represented at many resolutions, but coarse-graining risks discarding structure relevant to neural dynamics. Here we ask at what resolution a connectome preserves what it predicts about neural activity. In C. elegans, direct synaptic connectivity poorly predicts whether stimulating one neuron evokes a response in another. A parameter-free predictor based on paths through the connectome performs substantially better, with most predictive information carried by two-step paths and by the arrangement rather than the number of connections. Coarse-graining neurons into biological cell classes preserves these predictions despite discarding path structure, whereas random partitions degrade them. Connectivity alone identifies alternative coarse-grainings that preserve prediction down to about nine groups corresponding to nervous-system subsystems. Partitions inferred from newly hatched animals preserve adult predictions, and lineage-based partitions in Drosophila preserve path structure better than random coarse-graining. Functional prediction can therefore survive at resolutions far coarser than those required to preserve structure.
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BibTeX
@misc{sale2026connectome,
title = {{Connectome structure constrains neural activity across scales}},
author = {C. Sale and M. J. Richardson},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.03.762695},
url = {https://www.biorxiv.org/content/10.64898/2026.11.03.762695v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Connectome structure constrains neural activity across scales
AU - C. Sale
AU - M. J. Richardson
PY - 2026
DA - 2026/11/03
PB - bioRxiv
DO - 10.64898/2026.11.03.762695
UR - https://www.biorxiv.org/content/10.64898/2026.11.03.762695v1
AB - Connectomes can be represented at many resolutions, but coarse-graining risks discarding structure relevant to neural dynamics. Here we ask at what resolution a connectome preserves what it predicts about neural activity. In C. elegans, direct synaptic connectivity poorly predicts whether stimulating one neuron evokes a response in another. A parameter-free predictor based on paths through the connectome performs substantially better, with most predictive information carried by two-step paths and by the arrangement rather than the number of connections. Coarse-graining neurons into biological cell classes preserves these predictions despite discarding path structure, whereas random partitions degrade them. Connectivity alone identifies alternative coarse-grainings that preserve prediction down to about nine groups corresponding to nervous-system subsystems. Partitions inferred from newly hatched animals preserve adult predictions, and lineage-based partitions in Drosophila preserve path structure better than random coarse-graining. Functional prediction can therefore survive at resolutions far coarser than those required to preserve structure.
ER -